Scientists at MIT and elsewhere have discovered extremely rare remnants of “proto Earth,” which formed about 4.5 billion years ago, before a colossal collision irreversibly altered the primitive planet’s composition and produced the Earth as we know today. Their findings, reported today in the journal Nature Geosciences, will help scientists piece together the primordial starting ingredients that forged the early Earth and the rest of the solar system.

Billions of years ago, the early solar system was a swirling disk of gas and dust that eventually clumped and accumulated to form the earliest meteorites, which in turn merged to form the proto Earth and its neighboring planets.

In this earliest phase, Earth was likely rocky and bubbling with lava. Then, less than 100 million years later, a Mars-sized meteorite slammed into the infant planet in a singular “giant impact” event that completely scrambled and melted the planet’s interior, effectively resetting its chemistry. Whatever original material the proto Earth was made from was thought to have been altogether transformed.

But the MIT team’s findings suggest otherwise. The researchers have identified a chemical signature in ancient rocks that is unique from most other materials found in the Earth today. The signature is in the form of a subtle imbalance in potassium isotopes discovered in samples of very old and very deep rocks. The team determined that the potassium imbalance could not have been produced by any previous large impacts or geological processes occurring in the Earth presently.

The most likely explanation for the samples’ chemical composition is that they must be leftover material from the proto Earth that somehow remained unchanged, even as most of the early planet was impacted and transformed.

“This is maybe the first direct evidence that we’ve preserved the proto Earth materials,” says Nicole Nie, the Paul M. Cook Career Development Assistant Professor of Earth and Planetary Sciences at MIT. “We see a piece of the very ancient Earth, even before the giant impact. This is amazing because we would expect this very early signature to be slowly erased through Earth’s evolution.”

The study’s other authors include Da Wang of Chengdu University of Technology in China, Steven Shirey and Richard Carlson of the Carnegie Institution for Science in Washington, Bradley Peters of ETH Zürich in Switzerland, and James Day of Scripps Institution of Oceanography in California.

A curious anomaly

In 2023, Nie and her colleagues analyzed many of the major meteorites that have been collected from sites around the world and carefully studied. Before impacting the Earth, these meteorites likely formed at various times and locations throughout the solar system, and therefore represent the solar system’s changing conditions over time. When the researchers compared the chemical compositions of these meteorite samples to Earth, they identified among them a “potassium isotopic anomaly.”

Isotopes are slightly different versions of an element that have the same number of protons but a different number of neutrons. The element potassium can exist in one of three naturally-occurring isotopes, with mass numbers (protons plus neutrons) of 39, 40, and 41, respectively. Wherever potassium has been found on Earth, it exists in a characteristic combination of isotopes, with potassium-39 and potassium-41 being overwhelmingly dominant. Potassium-40 is present, but at a vanishingly small percentage in comparison.

Nie and her colleagues discovered that the meteorites they studied showed balances of potassium isotopes that were different from most materials on Earth. This potassium anomaly suggested that any material that exhibits a similar anomaly likely predates Earth’s present composition. In other words, any potassium imbalance would be a strong sign of material from the proto Earth, before the giant impact reset the planet’s chemical composition.

“In that work, we found that different meteorites have different potassium isotopic signatures, and that means potassium can be used as a tracer of Earth’s building blocks,” Nie explains.

“Built different”

In the current study, the team looked for signs of potassium anomalies not in meteorites, but within the Earth. Their samples include rocks, in powder form, from Greenland and Canada, where some of the oldest preserved rocks are found. They also analyzed lava deposits collected from Hawaii, where volcanoes have brought up some of the Earth’s earliest, deepest materials from the mantle (the planet’s thickest layer of rock that separates the crust from the core).

“If this potassium signature is preserved, we would want to look for it in deep time and deep Earth,” Nie says.

The team first dissolved the various powder samples in acid, then carefully isolated any potassium from the rest of the sample and used a special mass spectrometer to measure the ratio of each of potassium’s three isotopes. Remarkably, they identified in the samples an isotopic signature that was different from what’s been found in most materials on Earth.

Specifically, they identified a deficit in the potassium-40 isotope. In most materials on Earth, this isotope is already an insignificant fraction compared to potassium’s other two isotopes. But the researchers were able to discern that their samples contained an even smaller percentage of potassium-40. Detecting this tiny deficit is like spotting a single grain of brown sand in a bucket rather than a scoop full of of yellow sand.

The team found that, indeed, the samples exhibited the potassium-40 deficit, showing that the materials “were built different,” says Nie, compared to most of what we see on Earth today.

But could the samples be rare remnants of the proto Earth? To answer this, the researchers assumed that this might be the case. They reasoned that if the proto Earth were originally made from such potassium-40-deficient materials, then most of this material would have undergone chemical changes — from the giant impact and subsequent, smaller meteorite impacts — that ultimately resulted in the materials with more potassium-40 that we see today. 

The team used compositional data from every known meteorite and carried out simulations of how the samples’ potassium-40 deficit would change following impacts by these meteorites and by the giant impact. They also simulated geological processes that the Earth experienced over time, such as the heating and mixing of the mantle. In the end, their simulations produced a composition with a slightly higher fraction of potassium-40 compared to the samples from Canada, Greenland, and Hawaii. More importantly, the simulated compositions matched those of most modern-day materials.

The work suggests that materials with a potassium-40 deficit are likely leftover original material from the proto Earth.

Curiously, the samples’ signature isn’t a precise match with any other meteorite in geologists’ collections. While the meteorites in the team’s previous work showed potassium anomalies, they aren’t exactly the deficit seen in the proto Earth samples. This means that whatever meteorites and materials originally formed the proto Earth have yet to be discovered.

“Scientists have been trying to understand Earth’s original chemical composition by combining the compositions of different groups of meteorites,” Nie says. “But our study shows that the current meteorite inventory is not complete, and there is much more to learn about where our planet came from.”

This work was supported, in part, by NASA and MIT.

• Berita Harian Online, 11 October 2025

• The Straits Times Online, 10 October 2025
• Lianhe Zaobao, 11 October 2025, Singapore, p11

• The Straits Times Online, 10 October 2025
• 8world Online, 10 October 2025
• Lianhe Zaobao, 11 October 2025, Singapore, p10

What does “home” mean in Singapore’s 60th year of nationhood? At the annual NUS Chua Thian Poh Community Leadership Centre (CTPCLC) Symposium this year, NUS students, alumni, faculty, guests and community partners, including representatives from the Chua Foundation, came together to explore this question, discovering that “home” is as much about neighbourliness and family as it is about digital connections and inclusive communities.

Held on 27 September 2025, the Symposium brought together more than 130 attendees, to reflect on the theme: The Idea of ‘Home’ @SG60.

Perspectives on belonging

The event opened with a panel discussion moderated by CTPCLC Director Associate Professor Chng Huang Hoon, featuring Guest-of-Honour Mr David Neo, Acting Minister for Culture, Community and Youth and Senior Minister of State for Education; Ms Chua Weiling, Director of Philanthropy at Chua Foundation and CEO of One Hill Capital Pte Ltd; and CTPCLC alumnus Mr Cheng Tian Wei.

Mr Neo emphasised the importance of balancing individual and collective responsibility in fostering a sense of belonging, noting the symbiotic relationship between the individual and the community. "The 'Me' succeeds because of the 'We', and willingly gives back to the 'We'. Policies and programmes may set the foundation but everyone must work together to build connections, strengthen trust and shape our collective future." he said.

Building on the emphasis on collective responsibility, Ms Chua highlighted that philanthropy should begin at home, noting that individuals can make their impact felt by giving back to Singapore before looking further afield. “If you want to start something, start from home,” she said. Echoing this ground-up ethos, Mr Cheng shared how everyday neighbourliness forms the bedrock of cohesion. He said, “With everyone looking out for their neighbours, and the people around them, we can build a kinder, more compassionate society.”

Together, their perspectives traced a clear vision of “home”, enabled by national structures, nurtured within families and communities, and sustained through compassionate community practice.

From kampung to cloud

Offering a contemporary twist, CTPCLC alumnus and 2023 Outstanding Young Alumni Award recipient, Mr Khoo Yi Feng, delivered a keynote address titled From Kampung to Cloud: Rethinking Home in an Age of Movement and Memory.

Mr Khoo noted that in today’s world, belonging extends beyond physical spaces. “As networked individuals, we fulfil our needs in multiple online communities. This challenges community leaders to design, cultivate and steward virtual spaces. The feeling of ‘home’ doesn’t just happen and is no longer just a physical place.”

Drawing on his experiences with mental health advocacy, he urged students, educators, and partners to co-create spaces—physical and virtual—where people feel safe, seen, supported, and empowered to “make good trouble”.

Diverse pathways to community impact

A highlight of the Symposium was a showcase of four student-led community projects, where NUS students applied classroom learning to tackle real-world social issues.

• Identifying the aftercare needs of families in their post-home ownership journey

CTPCLC students Mavis Chin Jun Qi (Arts and Social Sciences ’25) and Freddy Ow Yong Zhi Long (Arts and Social Sciences ’25) presented a study in partnership with the South Central Community Family Service Centre, to examine the aftercare needs of low-income families who completed the Keystart Home Ownership Programme. Using semi-structured interviews and focus group discussions, the team developed a three-phase aftercare model that focuses on strengthening financial literacy, building home maintenance skills, and deepening social connectedness to support families’ stability beyond the point of purchase.

• Negotiating memory and modernity: A stakeholder-centric redevelopment of Esplanade Park

A team from NUS Ridge View Residential College (RVRC), represented by Hazel Tio (Science ’26), investigated the urban tension between heritage preservation and modern development through the lens of the development of the Padang and Esplanade Park. Grounded in multi-layered historical analysis and the site’s recent national recognition and potential UNESCO status, the study proposed a stakeholder-driven, collaborative placemaking approach for Esplanade Park. By integrating the site’s history, context, and redevelopment needs with interactive installations and curated cultural experiences, the team outlined a path to revitalise the park, renewing civic identity while harmonising memory and modernity.

• Coming home empty: Perspectives of empty nesters who find renewed purpose in art

Partnering with Mama on Palette (MoP), a community supporting mothers’ artistic expression, CTPCLC students Chomel Chan (Arts and Social Sciences ’25), Shannon Foo Shao Wei (Business ’26), Denise Leong En Hui (Arts and Social Sciences ’25), Angie Lim Tze Yii (Arts and Social Sciences ’25) and Isabel Lui Yisha (Arts and Social Sciences ’25) explored the expected and lived experiences of female empty nesters in Singapore. Through semi-structured interviews with eight mothers, the research surfaced themes including spousal relationships, phases of transition, and self-fulfilment. This project highlighted how MoP fosters community and empowerment and pointed to opportunities to deepen support for current and future empty nesters. Recommendations included buddy systems, bonding programmes, and regular collaborative art projects—underscoring the role of artistic engagement in ensuring wellbeing and resilience.

• Nourishing rough sleepers in Singapore

  NUS College student Keicia Seek En-Ting (Arts and Social Sciences ’27) addressed food insecurity among rough sleepers, as part of her team’s Impact Experience project by working with Homeless Hearts of Singapore and food charities to co-develop a sustainable food distribution system. Guided by a needs assessment conducted directly with rough sleepers, the project aims to provide regular, nutritious meals that alleviate financial pressures and improve physical and mental well-being. A key outcome is a replicable distribution model designed to respond to community-identified needs and enable longer-term support.

Recognising achievement and community spirit

The symposium also celebrated the Class of 2025 graduates who completed the Certificate or Minor Degree in Community Development and Leadership. In her valedictory address, Isabel Lui (Arts and Social Sciences ’25) thanked professors, peers, and families for anchoring the cohort’s journey, and urged her classmates to carry that sense of belonging into every space—serving, collaborating, and building communities where others feel they belong. The event concluded with a networking lunch, where students, alumni, partners, and guests renewed connections and exchanged ideas to make the world a little more like home.

The 2025 symposium marked another key milestone in CTPCLC’s ongoing work to cultivate youth leaders who can bridge sectors, mobilise communities, and co-create solutions. With sustained support from the Chua Foundation and strong partnerships across NUS and the wider community, CTPCLC continues to empower students to play an active role in building an inclusive, resilient society, and contribute meaningfully to Singapore and beyond.

By NUS Chua Thian Poh Community Leadership Centre

The NUS Chua Thian Poh Community Leadership Centre offers both a Certificate and a Minor Degree in Community Development and Leadership. To date, 370 students have been awarded the Certificate in Community Development and Leadership and 59 of these students also received a Minor Degree in Community Development and Leadership.

For decades, synthetic biologists have been developing gene circuits that can be transferred into cells for applications such as reprogramming a stem cell into a neuron or generating a protein that could help treat a disease such as fragile X syndrome.

These gene circuits are typically delivered into cells by carriers such as nonpathogenic viruses. However, it has been difficult to ensure that these cells end up producing the correct amount of the protein encoded by the synthetic gene.

To overcome that obstacle, MIT engineers have designed a new control mechanism that allows them to establish a desired protein level, or set point, for any gene circuit. This approach also allows them to edit the set point after the circuit is delivered.

“This is a really stable and multifunctional tool. The tool is very modular, so there are a lot of transgenes you could control with this system,” says Katie Galloway, an assistant professor in Chemical Engineering at MIT and the senior author of the new study.

Using this strategy, the researchers showed that they could induce cells to generate consistent levels of target proteins. In one application that they demonstrated, they converted mouse embryonic fibroblasts to motor neurons by delivering high levels of a gene that promotes that conversion.

MIT graduate student Sneha Kabaria is the lead author of the paper, which appears today in Nature Biotechnology. Other authors include Yunbeen Bae ’24; MIT graduate students Mary Ehmann, Brittany Lende-Dorn, Emma Peterman, and Kasey Love; Adam Beitz PhD ’25; and former MIT postdoc Deon Ploessl.

Dialing up gene expression

Synthetic gene circuits are engineered to include not only the gene of interest, but also a promoter region. At this site, transcription factors and other regulators can bind, turning on the expression of the synthetic gene.

However, it’s not always possible to get all of the cells in a population to express the desired gene at a uniform level. One reason for that is that some cells may take up just one copy of the circuit, while others receive many more. Additionally, cells have natural variation in how much protein they produce.

That has made reprogramming cells challenging because it’s difficult to ensure that every cell in a population of skin cells, for example, will produce enough of the necessary transcription factors to successfully transition into a new cell identity, such as a neuron or induced pluripotent stem cell.

In the new paper, the researchers devised a way to control gene expression levels by changing the distance between the synthetic gene and its promoter. They found that when there was a longer DNA “spacer” between the promoter region and the gene, the gene would be expressed at a lower level. That extra distance, they showed, makes it less likely that transcription factors bound to the promoter will effectively turn on gene transcription.

Then, to create set points that could be edited, the researchers incorporated sites within the spacer that can be excised by an enzyme called Cre recombinase. As parts of the spacer are cut out, it helps bring the transcription factors closer to the gene of interest, which turns up gene expression.

The researchers showed they could create spacers with multiple excision points, each targeted by different recombinases. This allowed them to create a system called DIAL, that they could use to establish “high,” “med,” “low” and “off” set points for gene expression.

After the DNA segment carrying the gene and its promoter is delivered into cells, recombinases can be added to the cells, allowing the set point to be edited at any time.

The researchers demonstrated their system in mouse and human cells by delivering the gene for different fluorescent proteins and functional genes, and showed that they could get uniform expression across the a population of cells at the target level.

“We achieved uniform and stable control. This is very exciting for us because lack of uniform, stable control has been one of the things that's been limiting our ability to build reliable systems in synthetic biology. When there are too many variables that affect your system, and then you add in normal biological variation, it’s very hard to build stable systems,” Galloway says.

Reprogramming cells

To demonstrate potential applications of the DIAL system, the researchers then used it to deliver different levels of the gene HRas^G12V to mouse embryonic fibroblasts. This HRas variant has previously been shown to increase the rate of conversion of fibroblasts to neurons. The MIT team found that in cells that received a higher dose of the gene, a larger percentage of them were able to successfully transform into neurons.

Using this system, researchers now hope to perform more systematic studies of different transcription factors that can induce cells to transition to different cell types. Such studies could reveal how different levels of those factors affect the success rate, and whether changing the transcription factors levels might alter the cell type that is generated.

In ongoing work, the researchers have shown that DIAL can be combined with a system they previously developed, known as ComMAND, that uses a feedforward loop to help prevent cells from overexpressing a therapeutic gene.

Using these systems together, it could be possible to tailor gene therapies to produce specific, consistent protein levels in the target cells of individual patients, the researchers say.

“This is something we’re excited about because both DIAL and ComMAND are highly modular, so you could not only have a well-controlled gene therapy that’s somewhat general for a population, but you could, in theory, tailor it for any given person or any given cell type,” Galloway says.

The research was funded, in part, by the National Institute of General Medical Sciences, the National Science Foundation, and the Institute for Collaborative Biotechnologies.

Lead poisoning was once thought to largely be a problem of the past, as the globe gradually weaned itself off leaded gasoline in road vehicles in 2021. But has global lead pollution truly been resolved?

A new study led by Dr Chen Mengli, a Research Fellow from the Tropical Marine Science Institute at the National University of Singapore (NUS), in collaboration with researchers from Imperial College London, University of Warwick, University of Oxford, Jadavpur University, University of Michigan, Ann Arbor, Hebrew University of Jerusalem, Massachusetts Institute of Technology, and University of Bristol, showed the answer is not yet: lead exposure remains a pressing public health and economic challenge in the 21^st century. The researchers estimated that ongoing childhood lead exposure costs the world more than US$3.4 trillion in lost economic potential each year, with disproportionate impacts on low- and middle-income countries.

Published in the Communications Earth & Environment on 30 September 2025, the findings suggest that without stronger safeguards, the ever-increasing demand for electrification and poorly regulated recycling of lead-containing products could entrench global inequalities and set back decades of progress in children’s health. To avert this, the researchers proposed a four-pronged strategy that policymakers and industries can act on today.

Lessons from history

Lead has been woven into human society for thousands of years, from the plumbing systems of the Roman Empire to the paints, pipes and industrial alloys still in use today. Its widespread use has left a toxic trail. Some of the earliest mass poisonings were linked to contaminated food and drink in Europe centuries ago. But the most recent incident came with the introduction of tetraethyl lead in gasoline in the 1920s, which for decades spewed millions of tonnes of the metal into the atmosphere.

By the 1970s, children across the world carried dangerously high blood lead levels, and the repercussions were severe, causing neurological damage, impaired development and countless premature deaths. The eventual ban on leaded gasoline, completed worldwide only in 2021, is heralded as one of the great public health victories of the modern era. Importantly, it showed that determined, coordinated global action could reduce exposure and save lives.

However, the team noted that the celebration of a “lead-free” world was premature. While blood lead levels fell in many high-income countries, they plateaued or even rose again in parts of Asia, Africa and Latin America. Legacy contamination from soils and infrastructure, coal combustion, numerous lead-laden products such as leaded paint, and informal recycling of lead-acid batteries and e-wastes have all kept exposure alive.

“The perception that the problem was solved has to change. New sources of exposure continue to emerge and the historical emitted lead keeps redistributing through various natural processes,” added Dr Chen, who is also from the Department of Geography, Faculty of Arts and Social Sciences at NUS.

Today’s exposure and economic toll

Lead production today exceeds 16 million tonnes a year, with about 85 per cent going into lead–acid batteries that power vehicles, telecommunications and backup energy systems. Annual production now exceeds the total lead emitted during the entire era of leaded gasoline.

Though these items can be recycled, much of the reprocessing occurs under unsafe conditions, particularly in low- and middle-income countries. Informal recycling sites, often located near homes and schools, expose workers and surrounding communities to hazardous levels of lead. Coal combustion, contaminated soils and the continued sale of lead-laden paints, toys, and even food products, further compound the risks.

The researchers noted from numerous literatures that health consequences are most severe for children. Even at low levels, lead can damage the developing brain, lowering IQ, impairing learning and contributing to behavioural issues. This burden is often carried across one’s lifetime as the effects are irreversible. In particular, the team estimated that childhood exposure today translates into a global economic loss exceeding US$3.4 trillion annually, equivalent to over 2 per cent of the world’s GDP.

Four-pronged approach to curb a resurgence

The team highlighted that recognising the continuing risks is the first step towards preventing another global health crisis. The study outlined four urgent areas for action to safeguard public health and reduce inequality:

1. Manage the life cycle of lead-containing products. With demand for batteries and electronics rising, stronger oversight is needed to minimise leakage during production, use and disposal.

2. Eliminate unsafe and illicit sources. Informal recycling and lead-laden goods such as lead paints, glazed ceramics and adulterated spices continue to expose millions to hazardous levels of lead.

3. Strengthen monitoring and community involvement. Early detection of lead leakage is often underfunded. Advances in low-cost sensors and machine-learning-based tools, combined with local knowledge, can help identify and address hotspots more effectively.

4. Capture the full socio-economic cost. Lead exposure disproportionately harms disadvantaged populations. Better models and population-level data are needed to quantify long-term impacts on health, education and productivity, as well as guiding equitable policy responses.

“The world rightly celebrated the phase-out of leaded gasoline as a triumph of international cooperation,” she said. “But the problem of lead exposure has not yet gone away. Unless we remain vigilant about both new sources of exposure and the legacy of lead in the environment, we may risk repeating the same tragedy,” Dr Chen emphasised.

Four faculty members from the Department of Biomedical Engineering at the College of Design and Engineering in the National University of Singapore have been conferred prestigious awards by the International Federation for Medical and Biological Engineering (IFMBE) for their exemplary contributions to medical and biological engineering. The awards were presented on 29 September 2025 during the World Congress on Medical Physics and Biomedical Engineering 2025, which was held in Adelaide, Australia. IFMBE is one of the world’s largest federation of national and transnational societies in biomedical engineering, and it also a Non-Governmental Organisation (NGO) for the United Nations and the World Health Organization (WHO).

Emeritus Professor James Goh (left) received the IFMBE Honorary Life Member Award, in recognition of his distinguished leadership and long-standing contributions to the global biomedical engineering community.

Professor Lim Chwee Teck was presented with the IFMBE Otto Schmitt Award in recognition of his pioneering work in mechanobiology, microfluidics, and wearable technologies, as well as his leadership, innovation, and impact in advancing the field. Prof Lim is also NUSS Chair Professor, Director of the Institute for Health Innovation & Technology (iHealthtech), and Principal Investigator at Mechanobiology Institute.

Professor Li Jun received the IFMBE Vladimir K. Zworykin Award for his outstanding research on supramolecular self-assembled nanomaterials and hydrogels, which have advanced the fields of nanomedicine and sustainable agriculture.

Assistant Professor Andy Tay was honoured with the IFMBE–IAMBE Early Career Award, which recognises promising young researchers within seven years of completing their PhD. Asst Prof Tay is also a Principal Investigator at iHealthtech.

Read more.

For seven decades, the Department of Civil and Environmental Engineering (CEE) in the College of Design and Engineering at the National University of Singapore has played a pivotal role in Singapore’s transformation and contributed to advancements in global engineering. To commemorate this significant milestone, CEE organised the 70th Anniversary Symposium 2025 on 10 October 2025, in partnership with the Professional Engineers Board (PEB) and The Institution of Engineers, Singapore (IES).

Held at the Marina Bay Sands Expo & Convention Centre, the event brought together partners from the government and industry sectors, as well as students from primary and secondary schools, pre-university institutions and polytechnics.

Comprising three components – the symposium, an exhibition and a student fair – the event provided a unique platform where academia, government, and industry came together to share perspectives and innovative solutions, explore collaborative approaches and excite young talents who will shape the future of the built environment sector.

Guest-of-Honour Ms Indranee Rajah, Minister in the Prime Minister’s Office and Second Minister for Finance and National Development, said, “As we face challenges like climate change and resource scarcity, the role of civil and environmental engineers has never been more critical – or more exciting.” Highlighting that built environment professionals “shape how we live, work, and play” and their work “creates the infrastructure that connects communities, and the solutions that protect our environment”, Ms Rajah discussed the various efforts to uplift the built environment sector.

Professor Teo Kie Leong, Dean of the College of Design and Engineering at NUS, expressed his appreciation to CEE’s partners for their unstinting support. “Together, we have nurtured talent, advanced innovation, and shaped Singapore’s built environment into one we can all take pride in,” he said.

“Singapore has been our living laboratory and our shared mission – to engineer a safe, sustainable, and resilient home for generations to come. Today’s symposium reflects that mission and our direction forward,” said Professor Richard Liew, Head of CEE. He added that CEE remained committed to three key thrusts, namely, education, research, and engagement, over the next decade.

A resilient, sustainable and innovative Singapore

The symposium featured speakers from key agencies in Singapore's built environment sector — namely, Building and Construction Authority (BCA), Housing & Development Board (HDB), PUB, Home Team Science and Technology Agency, Land Transport Authority, Urban Redevelopment Agency, and JTC Corporation — who shared important milestones achieved, addressed the current challenges facing the industry, and outlined strategies to build a more resilient and sustainable future for Singapore.

The concluding session of the symposium featured academic and industry leaders discussing the innovative strategies and collaborative approaches necessary for the sector to advance and succeed in an increasingly complex and evolving environment.

As part of his symposium lecture, Associate Professor Raymond Ong from CEE outlined the research directions that will help tackle current and future challenges. His focus was on sustainability and green technologies, robotics and automation, as well as intelligent sensing and autonomy within the built environment. Additionally, he emphasised the importance of developing industry capabilities in the context of an ageing infrastructure and an ageing society.

Innovation comes alive

An interesting highlight of the event was an exhibition featuring key innovations that shaped Singapore today and will drive the nation’s progress into the future. Among the exhibits were:

• Green cement and 3D printing (CEE)

  This technology pushes the limit of recycling percentage in cement, relying solely on local waste. This innovation not only reduces the carbon footprint of the built environment and wastes destined for Semakau but also reflects CEE’s strong commitment to advancing green and sustainable technologies.

• Periodic inspection and defect detection of coastal infrastructure using autonomous underwater vehicles (CEE, BeeX and Delta Marine Consultants)

  A groundbreaking approach to enhance the monitoring and protection of Singapore’s coastal infrastructure is the deployment of advanced Hovering Autonomous Underwater Vehicles (HAUVs) to conduct precise and thorough inspections of submerged structures, reducing reliance on human divers and lowering inspection costs. This innovative solution aims to set a new standard for coastal protection, ensuring the long-term safety and resilience of Singapore's shores.

• Centre for Resource Circularity and Resilience (CEE)

  A series of low-carbon cement, carbon-mineralised aggregates, and aggregates developed from 100 per cent local wastes could be used to address the waste and resource challenges of Singapore. These innovations offer sustainable pathways to upcycle large volumes of excavated marine clay from upcoming megaprojects, turning what was once waste into high-value construction resources.

• First on-site 3D printed concrete building (CEE and Woh Hup)

  The Norwood Grand Project, located at Champions Way, is Woh Hup’s first on-site 3D concrete printing project in Singapore approved by BCA. CEE researchers contributed their expertise in materials engineering and structural performance, ensuring adherence to local standards while integrating cutting-edge technology.

Inspiring future engineering talents through fun and play

Close to 300 students from around 30 primary and secondary schools, pre-university institutions and polytechnics participated in the student fair, which was designed to immerse students in the world of engineering. These students had the opportunity to engage in a variety of hands-on activities, dynamic interactive demonstrations and connect with engineers as well as young professionals.

These experiences were tailored to spark curiosity and enthusiasm, encouraging students to explore the principles of engineering in action while discovering how these concepts apply to real-world situations, from desalination to tunnelling to protecting Singapore’s shores.

Scientists from the Singapore Centre for Environmental Life Sciences Engineering (SCELSE) – a biofilm & microbiome research centre – and the National University of Singapore (NUS), have uncovered a surprising strategy plants use to thrive when an essential nutrient — sulphur — is in short supply.

The team discovered that when soil microbes compete with each other in the rhizosphere (the soil surrounding plant roots), they release a well-known compound called glutathione. This compound enhances plant growth under sulphur-deficient conditions. The catch: while plants benefit, some microbes lose out in their own growth.

The researchers call this balancing act a “trans-kingdom fitness trade-off” — where one kingdom of life (microbes) sacrifices part of its growth, while another (plants) gains resilience.

The global problem: declining sulphur in soils

Sulphur is essential for plant growth, just like nitrogen and phosphorus. It supports protein synthesis, vitamin production, and stress resistance.

Historically, sulphur pollution from industrial emissions replenished soils worldwide. But with cleaner energy and stricter air-quality regulations, atmospheric sulphur levels have dropped. While good for air quality and human health, this has unintentionally reduced natural sulphur deposits in agricultural soils.

Over time, crops have drawn down existing soil sulphur, leaving soils deficient. To compensate, farmers increasingly apply synthetic sulphur-based fertilisers. These short-term fixes come with costs: runoff from farmlands contaminates rivers, lakes, and ecosystems, exacerbating environmental degradation.

The new discovery: a microbial boost

The SCELSE-led study, published in Cell Host & Microbe on 26 September 2025, provides a novel mechanistic explanation of how plants and microbes jointly navigate nutrient stress. The researchers found that when soil bacteria compete for nutrients, they release glutathione — a compound that boosts plant growth under sulphur-deficient conditions, even though it reduces bacterial growth.

This improvement in plant fitness came at the cost of bacterial fitness — a biological trade-off across kingdoms of life.

“This work introduces the concept of a trans-kingdom fitness trade-off and provides a mechanistic explanation for it,” said first author Arijit Mukherjee, who was a PhD student at SCELSE and the NUS Department of Biological Sciences when the study was conducted. “Plant fitness isn’t just about the plant itself — it’s about the whole community of microbes around it. Understanding these trade-offs helps us design better microbial solutions for resilient crops.”

Why it matters

Such trade-offs are likely widespread across host–microbe systems, not just in plants, and may represent hidden strategies by which holobionts (hosts and their associated microbes) adapt collectively to environmental cues.

For agriculture, this insight is powerful: instead of relying on chemical fertilisers, researchers can design microbial consortia (or “cocktails”) that naturally boost crop health under nutrient stress. This nature-based solution can reduce fertiliser use, improve soil health, and contribute to global food security.

Assoc Prof Sanjay Swarup, Principal Investigator at SCELSE, explained, “This study provides a blueprint for sustainable agriculture. By tapping into natural plant–microbe partnerships, we can reduce fertiliser use, protect ecosystems, and still secure global food supplies.”

From discovery to application: patent filed

To translate this breakthrough into practice, the team has filed a patent covering applications of this plant–microbe mechanism in agriculture. This will enable the development of bio-based products that support crops in sulphur-deficient soils, reducing reliance on chemical inputs.

“By considering not only microbial functions but also their interactions, we can design more effective microbial consortia for agriculture,” added Assoc Prof Swarup, who is also the Deputy Director for NUS Environmental Research Institute (NERI) and a faculty member of the NUS Department of Biological Sciences. “This is the path toward resilient, climate-ready farming.”

• 8world Online, 9 October 2025

• Lianhe Zaobao, 9 October 2025, Singapore, p8

• Lianhe Zaobao, 9 October 2025, Singapore, p10

The National University of Singapore (NUS) has appointed Professor Joseph Liow as the third Dean of the Lee Kuan Yew School of Public Policy (LKYSPP), with effect from 15 October 2025.

A renowned scholar of international relations and Asian strategic affairs, Prof Liow brings to LKYSPP a distinguished record of academic excellence and institutional leadership experience. He will succeed Associate Professor Leong Ching who has served as Acting Dean of LKYSPP since 1 July 2025.

Prior to joining NUS, Prof Liow spent 28 years of his academic career at the Nanyang Technological University (NTU), progressing from a researcher to Chair Professor, and advancing towards senior leadership roles which include Dean of the NTU S. Rajaratnam School of International Studies, and Dean of the NTU College of Humanities, Arts and Social Sciences. Prof Liow has joined LKYSPP as the Wang Gungwu Professor in East Asian Affairs on 1 October 2025.

His research specialises in subjects of Muslim societies and politics in Southeast Asia, the international relations of Southeast Asia, US foreign policy, and the geopolitics of East Asia and the Indo-Pacific. Widely regarded as a leading voice on Southeast Asian affairs, Prof Liow’s insights on these matters have informed and influenced policymakers, diplomats, and international institutions across the region.

Beyond academia, Prof Liow has served as an advisor and commentator on regional and global policy issues, including engagements with international organisations, think tanks, and government agencies. His ability to bridge scholarly rigour with real-world relevance has made him a trusted interlocutor in global policy circles.

Prof Liow is currently Chairman of the Middle East Institute at NUS, a position he assumed in September 2024, and previously held the Tan Kah Kee Chair in Comparative and International Politics at NTU.

In his new role, Professor Liow will lead LKYSPP in advancing its mission to develop future public leaders, contribute cutting-edge research, and engage with pressing policy challenges across Asia and beyond.

NUS President, Professor Tan Eng Chye, said: “Professor Joseph Liow has built a stellar career in advancing academic thinking on regional and global affairs, and engaging with the public, policymakers, academics and students in constructive and impactful ways. His academic breadth, respected scholarship, outstanding leadership and genuine commitment to public service make him uniquely suited to lead the Lee Kuan Yew School of Public Policy into its next chapter. I am confident he will build on the School’s strong foundations and further its mission as a centre of excellence in public policy education, research and engagement.”

“The University also expresses its deep appreciation to Assoc Prof Leong for her strong stewardship and commitment to the School as Acting Dean during this period of transition. Her leadership at this pivotal time has put the School on a strong footing for the trajectory ahead,” Prof Tan added.

Reflecting on his appointment as Dean, Professor Liow said: “It is a privilege to join the Lee Kuan Yew School of Public Policy, an institution that plays a vital role in shaping policy discourse and public leadership. These are exciting yet challenging times for Asia and the rest of the world. As societies grapple with complex challenges and transitions on issues like sustainability, technology and geopolitics, public policy and governance has never been more critical or contested. I look forward to working closely with the School’s outstanding faculty, students, staff and alumni to build on its strong foundations and expand its regional and global contributions.”

Founded in 2004, the NUS Lee Kuan Yew School of Public Policy is one of Asia’s leading institutions for public policy education and research.

Please refer to Annex A for the biography of Prof Joseph Liow.

Associate Professor Gene-Wei Li has accepted the position of associate head of the MIT Department of Biology, starting in the 2025-26 academic year. 

Li, who has been a member of the department since 2015, brings a history of departmental leadership, service, and research and teaching excellence to his new role. He has received many awards, including a Sloan Research Fellowship (2016), an NSF Career Award (2019), Pew and Searle scholarships, and MIT’s Committed to Caring Award (2020). In 2024, he was appointed as a Howard Hughes Medical Institute (HHMI) Investigator. 

“I am grateful to Gene-Wei for joining the leadership team,” says department head Amy E. Keating, the Jay A. Stein (1968) Professor of Biology and professor of biological engineering. “Gene will be a key leader in our educational initiatives, both digital and residential, and will be a critical part of keeping our department strong and forward-looking.” 

A great environment to do science

Li says he was inspired to take on the role in part because of the way MIT Biology facilitates career development during every stage — from undergraduate and graduate students to postdocs and junior faculty members, as he was when he started in the department as an assistant professor just 10 years ago. 

“I think we all benefit a lot from our environment, and I think this is a great environment to do science and educate people, and to create a new generation of scientists,” he says. “I want us to keep doing well, and I’m glad to have the opportunity to contribute to this effort.” 

As part of his portfolio as associate department head, Li will continue in the role of scientific director of the Koch Biology Building, Building 68. In the last year, the previous scientific director, Stephen Bell, Uncas and Helen Whitaker Professor of Biology and HHMI Investigator, has continued to provide support and ensured a steady ramp-up, transitioning Li into his new duties. The building, which opened its doors in 1994, is in need of a slate of updates and repairs. 

Although Li will be managing more administrative duties, he has provided a stable foundation for his lab to continue its interdisciplinary work on the quantitative biology of gene expression, parsing the mechanisms by which cells control the levels of their proteins and how this enables cells to perform their functions. His recent work includes developing a method that leverages the AI tool AlphaFold to predict whether protein fragments can recapitulate the native interactions of their full-length counterparts.  

“I’m still very heavily involved, and we have a lab environment where everyone helps each other. It’s a team, and so that helps elevate everyone,” he says. “It’s the same with the whole building: nobody is working by themselves, so the science and administrative parts come together really nicely.” 

Teaching for the future

Li is considering how the department can continue to be a global leader in biological sciences while navigating the uncertainty surrounding academia and funding, as well as the likelihood of reduced staff support and tightening budgets.

“The question is: How do you maintain excellence?” Li says. “That involves recruiting great people and giving them the resources that they need, and that’s going to be a priority within the limitations that we have to work with.” 

Li will also be serving as faculty advisor for the MIT Biology Teaching and Learning Group, headed by Mary Ellen Wiltrout, and will serve on the Department of Biology Digital Learning Committee and the new Open Learning Biology Advisory Committee. Li will serve in the latter role in order to represent the department and work with new faculty member and HHMI Investigator Ron Vale on Institute-level online learning initiatives. Li will also chair the Biology Academic Planning Committee, which will help develop a longer-term outlook on faculty teaching assignments and course offerings. 

Li is looking forward to hearing from faculty and students about the way the Institute teaches, and how it could be improved, both for the students on campus and for the online learners from across the world. 

“There are a lot of things that are changing; what are the core fundamentals that the students need to know, what should we teach them, and how should we teach them?” 

Although the commitment to teaching remains unchanged, there may be big transitions on the horizon. With two young children in school, Li is all too aware that the way that students learn today is very different from what he grew up with, and also very different from how students were learning just five or 10 years ago — writing essays on a computer, researching online, using AI tools, and absorbing information from media like short-form YouTube videos. 

“There’s a lot of appeal to a shorter format, but it’s very different from the lecture-based teaching style that has worked for a long time,” Li says. “I think a challenge we should and will face is figuring out the best way to communicate the core fundamentals, and adapting our teaching styles to the next generation of students.” 

Ultimately, Li is excited about balancing his research goals along with joining the department’s leadership team, and knows he can look to his fellow researchers in Building 68 and beyond for support.

“I’m privileged to be working with a great group of colleagues who are all invested in these efforts,” Li says. “Different people may have different ways of doing things, but we all share the same mission.” 

By Asst Prof Reuben Ng from the Lee Kuan Yew School of Public Policy at NUS and Lead Scientist at the Lloyd’s Register Foundation Institute for the Public Understanding of Risk at NUS

• The Straits Times, 8 October 2025, Opinion, pB2

• Suria News Online, 7 October 2025
• CNA Online, 7 October 2025

• CNA, 7 October 2025

• 8world Online, 7 October 2025

• Suria News Online, 7 October 2025

• CNA938, 7 October 2025

• CNA Online, 8 October 2025

• The Straits Times, 8 October 2025, Singapore, pA14

• The Business Times, 8 October 2025, p28

• Lianhe Zaobao, 8 October 2025, Focus, p3

• Berita Harian, 8 October 2025, p2

• Tamil Murasu, 8 October 2025, p3

The potential for future breakthroughs benefitting society was the cornerstone of the Distinguished Undergraduate Research Prize (DURP) and Outstanding Undergraduate Researcher Prize (OURP) which were awarded to 47 NUS undergraduate research projects this year. A total of 91 entries were submitted and these projects were rigorously evaluated based on stringent criteria, such as impact factor, understanding of the subject, evidence of critical and independent thinking, originality and significance, and accolades received. Two outstanding projects were awarded the DURP and 45 projects received the OURP.

Established almost 20 years ago, the OURP recognises students who demonstrate outstanding participation and achievement in research. These exemplary research projects were undertaken through a range of initiatives, including the Undergraduate Research Opportunities Programme (UROP) and Field Service Projects. The DURP was launched in Academic Year 2023/24 to recognise top-ranked research projects in OURP.

NUS News highlights three student projects from the NUS School of Computing (NUS Computing) and the NUS Yong Loo Lin School of Medicine (NUS Medicine), that tackles real-world issues, such as hepatocellular carcinoma, cardiovascular risk factors, and the testing of concurrent software.

Advancing liver cancer diagnosis and treatment

Lin Hong Yi, who had recently graduated from NUS Medicine, won the DURP (Individual Category) for his project on primary liver cancer, or hepatocellular carcinoma (HCC). This type of cancer is challenging to treat because of its complex composition from varied gene expression. Conducted as part of the larger Precision Medicine In Liver Cancer Across An Asia-Pacific Network 2.0 (PLANet) programme, Hong Yi’s study analysed human liver cancer tissues in great detail, focusing on both gene activity and a specific type of “epigenetic switch” that helps control whether genes are turned on or off.

He discovered several cancer-promoting genes that are switched on through these epigenetic mechanisms, with liver cancers caused by different factors — such as Hepatitis B virus infection or fatty liver disease — showing distinct patterns of gene activation. Certain regions of the genome that are usually inactive in normal cells but appear to play a key role in activating cancer genes in HCC were also identified. These findings could potentially pave way for future studies to develop better tests, earlier detection, and more personalised treatments for HCC.

In the study, Hong Yi extracted DNA from cancer cells and used a combination of chemical treatments and ultrasonic waves to isolate specific regions of interest. The samples were then decoded using advanced sequencing technology, with the genetic information analysed with powerful computer algorithms to identify gene regions that are commonly activated by these epigenetic switches.

Enhancing cardiovascular health

NUS Medicine students Yiming Chen, Srinithy Nagarajan, Jayanth Jayabaskaran and NUS Medicine alumna Rachel Goh received the DURP (Group Category) for their project “The Global Syndemic of Modifiable Cardiovascular Risk Factors projected from 2025 to 2050”.

Cardiovascular diseases (CVDs) are the leading cause of global mortality and understanding the trends driving CVDs is essential in designing effective countermeasures. This group project forecasts trends in five key modifiable cardiovascular risk factors — high systolic blood pressure, low-density lipoprotein cholesterol, body mass index, fasting glucose levels, and tobacco use — over the next 25 years till 2050. Modifiable risk factors refer to factors that can be controlled by individuals, in contrast to non-modifiable factors such as genetic risks.

The group found that despite improved management of CVDs leading to a projected decline in rates of disability-adjusted life years (a measure of the overall burden of disease in a population) across all cardiovascular risk factors, the overall disability-adjusted life years will continue to rise due to population growth and ageing. High systolic blood pressure and BMI are the fastest-growing contributors to these trends, emphasising the urgent need for tailored, region- and demographic-specific cardiovascular prevention and intervention to curb global cardiometabolic risk.

The historical and projection data for the study was taken from the Institute for Health Metrics and Evaluation Global Burden of Disease database, with the largest contributors measured based on deaths and disability associated life years (i.e. years of life lost to disability and death from disease) attributable to each risk factor. Further studies are also being conducted to examine the impacts of these risk factor trends on future cardiovascular health.

Boosting software reliability

Jed Koh, a Computer Science major at NUS Computing was awarded the OURP in the Individual Category for his project “Property Testing for Trace Regular Languages”. He conducted the research project as part of UROP last year when he was a third-year student. Working with Assistant Professor Umang Mathur, Presidential Young Professor at NUS Computing, on concurrency testing and trace languages, Jed combined both theoretical depth and practical applications for his project.

To meet the rising demands of modern software such as cloud services and web browsers, concurrency is increasingly leveraged, enabling different components of a software application to run simultaneously for a shared task. However, this exponentially increases the number of ways in which individual components interact, concealing software bugs which are notoriously hard to find.

Jed’s project, one of the top-ranked OURP projects, aims to enhance the reliability of modern software and detect concurrency bugs early in the software development cycle. Existing techniques that attempt to find these bugs tend to be extremely resource-intensive, requiring a lot of time, computing and monetary resources. Hence, he proposed an algorithmic framework to build fast, scalable, randomised algorithms to detect a large class of concurrency bugs that routinely impact software correctness and usability. As part of the formal analysis of these algorithms, he proved mathematical theorems that show that the error rates of these algorithms can be made arbitrarily small.

Experience the Excitement of Research

Could you be the next to make a similarly groundbreaking discovery? Extolling the additional benefits of research, Mr John Caines, Assistant Senior Manager, Research Experience (REx) said, “Undertaking research activities is incredibly meaningful and valuable — not just because of the potential outsized impact on solving real-life problems, but also educational and employability advantages when entering the workforce.”

To explore a multiverse of possibilities in research with UROP and REx today, visit NUS Undergraduate Research to learn more.

NUS students seeking career and internship opportunities will now benefit from a refreshed format for the NUS Career Fest, which will be held twice annually starting this academic year. 

The revamped flagship event organised by the NUS Centre for Future-ready Graduates (CFG) aims to enhance support for students in their career journeys by providing them with more opportunities to engage with employers, boost their professional development, and prepare for the workforce.

“We are encouraged that employer demand remains strong despite the job market uncertainties and disruptions AI brings to entry-level roles. More than 230 organisations are participating in NUS Career Fest Oct 2025. By running NUS Career Fest twice yearly, alongside faculty-specific fairs and combined with a vibrant, year-round campus recruitment calendar, we are creating more touchpoints for students to connect with employers. Ahead of the fair, CFG conducted workshops with a focus on strengthening the life skills as well as AI readiness including how to use AI responsibly and effectively in job search and early career roles.  These programmes are designed to help our students stand out, learn boldly and adapt confidently into a fast-moving work world”, said Joan Tay, Senior Director, NUS Centre for Future-ready Graduates.

A theme for the times: From Gen Z to Gen AI

Held from 8 to 9 October 2025 at two venues - the Stephen Riady Centre in NUS University Town and the Engineering Auditorium Atrium at the College of Design and Engineering - this year’s fair carried the timely theme “From Gen Z to Gen AI: The Prompt to Your Next Great Career”.

The theme underscores how today’s graduates are entering a workforce where generational strengths such as creativity, adaptability and digital fluency intersect with the transformative potential of artificial intelligence (AI).

Over the two days, students have the opportunity to meet with more than 230 participating employers across 20 industries, ranging from established sectors such as finance, engineering, and the public service, to fast-growing domains like AI, sustainability, and digital commerce.

On day one, thousands of students streamed through the booths, exploring internships, full-time roles, and career pathways they might not have considered before. Christopher Zhang, a Year 1 student from Faculty of Arts and Social Sciences reflected: “I think it’s a great idea to hold the fair in October because it aligns well with the cycle of hiring interns. Many companies start preparing for internship recruitment during the semester break in January, when listings are released. Attending the fair early gives me the chance to plan ahead, start shortlisting opportunities, preparing my résumé, and thinking about the types of internships I want to pursue. When February comes around, it’s more about refining the details and identifying specific companies I’d like to join. The experience also made me realise how important it is to stand out, to secure a role in my desired industry. It’s not just about being prepared but about understanding what more I can do to differentiate myself from others.”

Employers, in turn, were eager to connect with NUS talent. Pearlyn Wong, Talent Acquisition Senior Manager at SimplifyNext, said, “Our experience with NUS students has been genuinely energising. They bring a strong foundation in analytical thinking and an impressive ability to connect ideas across disciplines – from technology and data to human behaviour and design. What really stood out was how they responded to experiential challenges. In settings like our Agentic AI Hackathon or internships, NUS students quickly move from theory to practice – asking thoughtful questions and showing the kind of intellectual curiosity that thrives in an environment like SimplifyNext. We look for people who are not just good at solving problems, but who reframe them. And that’s something we’ve consistently seen among NUS students. Their adaptability, teamwork, and willingness to learn by doing mirror the same values that drive our own teams working on complex, AI-driven transformation projects.”

Many employers emphasised that beyond technical expertise, they are seeking graduates with critical thinking, adaptability, and learning agility.

Harnessing innovation in career preparation

A highlight of this October’s Career Fest was the launch of the CFG AI-Xplore chatbot, an AI-powered tool designed to provide students with instant access to career resources and guidance – from resume tips to sector-specific opportunities. Complementing in-person career advisory sessions, the chatbot supports NUS’ commitment to prepare students to thrive in an AI-augmented world.

Also drawing student interest was the CFG Career Quest game, created by Woon Cher Han, a Year 4 student from the School of Computing, which scored the winning entry in a CFG-organised competition earlier this year. In the game, players step into the shoes of a fresh graduate battling the Monster of Doubt and Fear, using CFG’s suite of offerings to overcome challenges. The interactive storyline resonated strongly with peers who recognised their own uncertainties mirrored in the gameplay.

Alongside these innovations, a range of fringe activities helped students sharpen their personal brand and networking skills. At the Career Pitch Stop, students practised their elevator pitch with real-time feedback from CFG career advisors. Interactive self-help kiosks, created in partnership with e2i, offered a gamified way for students to practise articulating their value proposition. Lifelong Learning SG guided students in discovering more about themselves and exploring career pathways that they might not have considered, while the NUS Lifelong Learning team advised soon-to-be alumni on courses to support professional and personal growth. To round off the experience at the Career Fest, students could also get a complimentary professional headshot.

Benedicta Edlyn Kurniawan, a Year 4 student from Faculty of Science, said: “Attending NUS Career Fest has really broadened my perspective. Many of the companies here are from industries I’ve never explored before. The fair made it so much easier to find out which companies are hiring and ask questions directly. I ended up discovering organisations with roles that genuinely interest me. I could even scan their QR codes on the spot to apply, which made the whole experience simple and productive.”

Sustaining the journey beyond Career Fest

Preparation for NUS Career Fest began weeks earlier, with workshops helping students refine resumes, strengthen elevator pitches, and polish personal brand statements. For the first time, post-event workshops have been introduced to ensure that students maintain momentum beyond the fair. These cover practical topics such as navigating job offers, salary negotiations, and building resilience during job searches.

Students can also explore international career opportunities at the Global Career Fair, between 13 and 23 October 2025, which offers access to employers from Japan, Hong Kong, India, Thailand, Mainland China, the United Arab Emirates, and beyond.

This will lead into the next edition of NUS Career Fest, slated to take place from first quarter of 2026. By anchoring two career fairs across the academic year, CFG is reshaping career preparation into a sustained journey of growth, discovery, and opportunity.

By NUS Centre for Future-ready Graduates

Understanding how interactions between the central nervous system and the immune system contribute to problems of aging, including Alzheimer’s disease, Parkinson’s disease, arthritis, and more, can generate new leads for therapeutic development, speakers said at MIT’s symposium “The Neuro-Immune Axis and the Aging Brain” on Sept 18.

“The past decade has brought rapid progress in our understanding of how adaptive and innate immune systems impact the pathogenesis of neurodegenerative disorders,” said Picower Professor Li-Huei Tsai, director of The Picower Institute for Learning and Memory and MIT’s Aging Brain Initiative (ABI), in her introduction to the event, which more than 450 people registered to attend. “Together, today’s speakers will trace how the neuro-immune axis shapes brain health and disease … Their work converges on the promise of immunology-informed therapies to slow or prevent neurodegeneration and age-related cognitive decline.”

For instance, keynote speaker Michal Schwartz of the Weizmann Institute in Israel described her decades of pioneering work to understand the neuro-immune “ecosystem.” Immune cells, she said, help the brain heal, and support many of its functions, including its “plasticity,” the ability it has to adapt to and incorporate new information. But Schwartz’s lab also found that an immune signaling cascade can arise with aging that undermines cognitive function. She has leveraged that insight to investigate and develop corrective immunotherapies that improve the brain’s immune response to Alzheimer’s both by rejuvenating the brain’s microglia immune cells and bringing in the help of peripheral immune cells called macrophages. Schwartz has brought the potential therapy to market as the chief science officer of ImmunoBrain, a company testing it in a clinical trial.

In her presentation, Tsai noted recent work from her lab and that of computer science professor and fellow ABI member Manolis Kellis showing that many of the genes associated with Alzheimer’s disease are most strongly expressed in microglia, giving it an expression profile more similar to autoimmune disorders than to many psychiatric ones (where expression of disease-associated genes typically is highest in neurons). The study showed that microglia become “exhausted” over the course of disease progression, losing their cellular identity and becoming harmfully inflammatory.

“Genetic risk, epigenomic instability, and microglia exhaustion really play a central role in Alzheimer’s disease,” Tsai said, adding that her lab is now also looking into how immune T cells, recruited by microglia, may also contribute to Alzheimer’s disease progression.

The body and the brain

The neuro-immune “axis” connects not only the nervous and immune systems, but also extends between the whole body and the brain, with numerous implications for aging. Several speakers focused on the key conduit: the vagus nerve, which runs from the brain to the body’s major organs.

For instance, Sara Prescott, an investigator in the Picower Institute and an MIT assistant professor of biology, presented evidence her lab is amassing that the brain’s communication via vagus nerve terminals in the body’s airways is crucial for managing the body’s defense of respiratory tissues. Given that we inhale about 20,000 times a day, our airways are exposed to many environmental challenges, Prescott noted, and her lab and others are finding that the nervous system interacts directly with immune pathways to mount physiological responses. But vagal reflexes decline in aging, she noted, increasing susceptibility to infection, and so her lab is now working in mouse models to study airway-to-brain neurons throughout the lifespan to better understand how they change with aging.

In his talk, Caltech Professor Sarkis Mazmanian focused on work in his lab linking the gut microbiome to Parkinson’s disease (PD), for instance by promoting alpha-synuclein protein pathology and motor problems in mouse models. His lab hypothesizes that the microbiome can nucleate alpha-synuclein in the gut via a bacterial amyloid protein that may subsequently promote pathology in the brain, potentially via the vagus nerve. Based on its studies, the lab has developed two interventions. One is giving alpha-synuclein overexpressing mice a high-fiber diet to increase short-chain fatty acids in their gut, which actually modulates the activity of microglia in the brain. The high-fiber diet helps relieve motor dysfunction, corrects microglia activity, and reduces protein pathology, he showed. Another is a drug to disrupt the bacterial amyloid in the gut. It prevents alpha synuclein formation in the mouse brain and ameliorates PD-like symptoms. These results are pending publication.

Meanwhile, Kevin Tracey, professor at Hofstra University and Northwell Health, took listeners on a journey up and down the vagus nerve to the spleen, describing how impulses in the nerve regulate immune system emissions of signaling molecules, or “cytokines.” Too great a surge can become harmful, for instance causing the autoimmune disorder rheumatoid arthritis. Tracey described how a newly U.S. Food and Drug Administration-approved pill-sized neck implant to stimulate the vagus nerve helps patients with severe forms of the disease without suppressing their immune system.

The brain’s border

Other speakers discussed opportunities for understanding neuro-immune interactions in aging and disease at the “borders” where the brain’s and body’s immune system meet. These areas include the meninges that surround the brain, the choroid plexus (proximate to the ventricles, or open spaces, within the brain), and the interface between brain cells and the circulatory system.

For instance, taking a cue from studies showing that circadian disruptions are a risk factor for Alzheimer’s disease, Harvard Medical School Professor Beth Stevens of Boston Children’s Hospital described new research in her lab that examined how brain immune cells may function differently around the day-night cycle. The project, led by newly minted PhD Helena Barr, found that “border-associated macrophages” — long-lived immune cells residing in the brain’s borders — exhibited circadian rhythms in gene expression and function. Stevens described how these cells are tuned by the circadian clock to “eat” more during the rest phase, a process that may help remove material draining from the brain, including Alzheimer’s disease-associated peptides such as amyloid-beta. So, Stevens hypothesizes, circadian disruptions, for example due to aging or night-shift work, may contribute to disease onset by disrupting the delicate balance in immune-mediated “clean-up” of the brain and its borders.

Following Stevens at the podium, Washington University Professor Marco Colonna traced how various kinds of macrophages, including border macrophages and microglia, develop from the embryonic stage. He described the different gene-expression programs that guide their differentiation into one type or another. One gene he highlighted, for instance, is necessary for border macrophages along the brain’s vasculature to help regulate the waste-clearing cerebrospinal fluid (CSF) flow that Stevens also discussed. Knocking out the gene also impairs blood flow. Importantly, his lab has found that versions of the gene may be somewhat protective against Alzheimer’s, and that regulating expression of the gene could be a therapeutic strategy.

Colonna’s WashU colleague Jonathan Kipnis (a former student of Schwartz) also discussed macrophages that are associated with the particular border between brain tissue and the plumbing alongside the vasculature that carries CSF. The macrophages, his lab showed in 2022, actively govern the flow of CSF. He showed that removing the macrophages let Alzheimer’s proteins accumulate in mice. His lab is continuing to investigate ways in which these specific border macrophages may play roles in disease. He’s also looking in separate studies of how the skull’s brain marrow contributes to the population of immune cells in the brain and may play a role in neurodegeneration.

For all the talk of distant organs and the brain’s borders, neurons themselves were never far from the discussion. Harvard Medical School Professor Isaac Chiu gave them their direct due in a talk focusing on how they participate in their own immune defense, for instance by directly sensing pathogens and giving off inflammation signals upon cell death. He discussed a key molecule in that latter process, which is expressed among neurons all over the brain.

Whether they were looking within the brain, at its border, or throughout the body, speakers showed that age-related nervous system diseases are not only better understood but also possibly better treated by accounting not only for the nerve cells, but their immune system partners. 

Understanding how interactions between the central nervous system and the immune system contribute to problems of aging, including Alzheimer’s disease, Parkinson’s disease, arthritis, and more, can generate new leads for therapeutic development, speakers said at MIT’s symposium “The Neuro-Immune Axis and the Aging Brain” on Sept 18.

“The past decade has brought rapid progress in our understanding of how adaptive and innate immune systems impact the pathogenesis of neurodegenerative disorders,” said Picower Professor Li-Huei Tsai, director of The Picower Institute for Learning and Memory and MIT’s Aging Brain Initiative (ABI), in her introduction to the event, which more than 450 people registered to attend. “Together, today’s speakers will trace how the neuro-immune axis shapes brain health and disease … Their work converges on the promise of immunology-informed therapies to slow or prevent neurodegeneration and age-related cognitive decline.”

For instance, keynote speaker Michal Schwartz of the Weizmann Institute in Israel described her decades of pioneering work to understand the neuro-immune “ecosystem.” Immune cells, she said, help the brain heal, and support many of its functions, including its “plasticity,” the ability it has to adapt to and incorporate new information. But Schwartz’s lab also found that an immune signaling cascade can arise with aging that undermines cognitive function. She has leveraged that insight to investigate and develop corrective immunotherapies that improve the brain’s immune response to Alzheimer’s both by rejuvenating the brain’s microglia immune cells and bringing in the help of peripheral immune cells called macrophages. Schwartz has brought the potential therapy to market as the chief science officer of ImmunoBrain, a company testing it in a clinical trial.

In her presentation, Tsai noted recent work from her lab and that of computer science professor and fellow ABI member Manolis Kellis showing that many of the genes associated with Alzheimer’s disease are most strongly expressed in microglia, giving it an expression profile more similar to autoimmune disorders than to many psychiatric ones (where expression of disease-associated genes typically is highest in neurons). The study showed that microglia become “exhausted” over the course of disease progression, losing their cellular identity and becoming harmfully inflammatory.

“Genetic risk, epigenomic instability, and microglia exhaustion really play a central role in Alzheimer’s disease,” Tsai said, adding that her lab is now also looking into how immune T cells, recruited by microglia, may also contribute to Alzheimer’s disease progression.

The body and the brain

The neuro-immune “axis” connects not only the nervous and immune systems, but also extends between the whole body and the brain, with numerous implications for aging. Several speakers focused on the key conduit: the vagus nerve, which runs from the brain to the body’s major organs.

For instance, Sara Prescott, an investigator in the Picower Institute and an MIT assistant professor of biology, presented evidence her lab is amassing that the brain’s communication via vagus nerve terminals in the body’s airways is crucial for managing the body’s defense of respiratory tissues. Given that we inhale about 20,000 times a day, our airways are exposed to many environmental challenges, Prescott noted, and her lab and others are finding that the nervous system interacts directly with immune pathways to mount physiological responses. But vagal reflexes decline in aging, she noted, increasing susceptibility to infection, and so her lab is now working in mouse models to study airway-to-brain neurons throughout the lifespan to better understand how they change with aging.

In his talk, Caltech Professor Sarkis Mazmanian focused on work in his lab linking the gut microbiome to Parkinson’s disease (PD), for instance by promoting alpha-synuclein protein pathology and motor problems in mouse models. His lab hypothesizes that the microbiome can nucleate alpha-synuclein in the gut via a bacterial amyloid protein that may subsequently promote pathology in the brain, potentially via the vagus nerve. Based on its studies, the lab has developed two interventions. One is giving alpha-synuclein overexpressing mice a high-fiber diet to increase short-chain fatty acids in their gut, which actually modulates the activity of microglia in the brain. The high-fiber diet helps relieve motor dysfunction, corrects microglia activity, and reduces protein pathology, he showed. Another is a drug to disrupt the bacterial amyloid in the gut. It prevents alpha synuclein formation in the mouse brain and ameliorates PD-like symptoms. These results are pending publication.

Meanwhile, Kevin Tracey, professor at Hofstra University and Northwell Health, took listeners on a journey up and down the vagus nerve to the spleen, describing how impulses in the nerve regulate immune system emissions of signaling molecules, or “cytokines.” Too great a surge can become harmful, for instance causing the autoimmune disorder rheumatoid arthritis. Tracey described how a newly U.S. Food and Drug Administration-approved pill-sized neck implant to stimulate the vagus nerve helps patients with severe forms of the disease without suppressing their immune system.

The brain’s border

Other speakers discussed opportunities for understanding neuro-immune interactions in aging and disease at the “borders” where the brain’s and body’s immune system meet. These areas include the meninges that surround the brain, the choroid plexus (proximate to the ventricles, or open spaces, within the brain), and the interface between brain cells and the circulatory system.

For instance, taking a cue from studies showing that circadian disruptions are a risk factor for Alzheimer’s disease, Harvard Medical School Professor Beth Stevens of Boston Children’s Hospital described new research in her lab that examined how brain immune cells may function differently around the day-night cycle. The project, led by newly minted PhD Helena Barr, found that “border-associated macrophages” — long-lived immune cells residing in the brain’s borders — exhibited circadian rhythms in gene expression and function. Stevens described how these cells are tuned by the circadian clock to “eat” more during the rest phase, a process that may help remove material draining from the brain, including Alzheimer’s disease-associated peptides such as amyloid-beta. So, Stevens hypothesizes, circadian disruptions, for example due to aging or night-shift work, may contribute to disease onset by disrupting the delicate balance in immune-mediated “clean-up” of the brain and its borders.

Following Stevens at the podium, Washington University Professor Marco Colonna traced how various kinds of macrophages, including border macrophages and microglia, develop from the embryonic stage. He described the different gene-expression programs that guide their differentiation into one type or another. One gene he highlighted, for instance, is necessary for border macrophages along the brain’s vasculature to help regulate the waste-clearing cerebrospinal fluid (CSF) flow that Stevens also discussed. Knocking out the gene also impairs blood flow. Importantly, his lab has found that versions of the gene may be somewhat protective against Alzheimer’s, and that regulating expression of the gene could be a therapeutic strategy.

Colonna’s WashU colleague Jonathan Kipnis (a former student of Schwartz) also discussed macrophages that are associated with the particular border between brain tissue and the plumbing alongside the vasculature that carries CSF. The macrophages, his lab showed in 2022, actively govern the flow of CSF. He showed that removing the macrophages let Alzheimer’s proteins accumulate in mice. His lab is continuing to investigate ways in which these specific border macrophages may play roles in disease. He’s also looking in separate studies of how the skull’s brain marrow contributes to the population of immune cells in the brain and may play a role in neurodegeneration.

For all the talk of distant organs and the brain’s borders, neurons themselves were never far from the discussion. Harvard Medical School Professor Isaac Chiu gave them their direct due in a talk focusing on how they participate in their own immune defense, for instance by directly sensing pathogens and giving off inflammation signals upon cell death. He discussed a key molecule in that latter process, which is expressed among neurons all over the brain.

Whether they were looking within the brain, at its border, or throughout the body, speakers showed that age-related nervous system diseases are not only better understood but also possibly better treated by accounting not only for the nerve cells, but their immune system partners. 

The MIT Schwarzman College of Computing and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) recently celebrated the launch of the MIT–MBZUAI Collaborative Research Program, a new effort to strengthen the building blocks of artificial intelligence and accelerate its use in pressing scientific and societal challenges.

Under the five-year agreement, faculty, students, and research staff from both institutions will collaborate on fundamental research projects to advance the technological foundations of AI and its applications in three core areas: scientific discovery, human thriving, and the health of the planet.

“Artificial intelligence is transforming nearly every aspect of human endeavor. MIT’s leadership in AI is greatly enriched through collaborations with leading academic institutions in the U.S. and around the world,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “Our collaboration with MBZUAI reflects a shared commitment to advancing AI in ways that are responsible, inclusive, and globally impactful. Together, we can explore new horizons in AI and bring broad benefits to society.”

“This agreement will unite the efforts of researchers at two world-class institutions to advance frontier AI research across scientific discovery, human thriving, and the health of the planet. By combining MBZUAI’s focus on foundational models and real-world deployment with MIT’s depth in computing and interdisciplinary innovation, we are creating a transcontinental bridge for discovery. Together, we will not only expand the boundaries of AI science, but also ensure that these breakthroughs are pursued responsibly and applied where they matter most — improving human health, enabling intelligent robotics, and driving sustainable AI at scale,” says Eric Xing, president and university professor at MBZUAI.

Each institution has appointed an academic director to oversee the program on its campus. At MIT, Philip Isola, the Class of 1948 Career Development Professor in the Department of Electrical Engineering and Computer Science, will serve as program lead. At MBZUAI, Le Song, professor of machine learning, will take on the role.

Supported by MBZUAI — the first university dedicated entirely to advancing science through AI, and based in Abu Dhabi, U.A.E. — the collaboration will fund a number of joint research projects per year. The findings will be openly publishable, and each project will be led by a principal investigator from MIT and one from MBZUAI, with project selections made by a steering committee composed of representatives from both institutions.

The MIT Schwarzman College of Computing and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) recently celebrated the launch of the MIT–MBZUAI Collaborative Research Program, a new effort to strengthen the building blocks of artificial intelligence and accelerate its use in pressing scientific and societal challenges.

Under the five-year agreement, faculty, students, and research staff from both institutions will collaborate on fundamental research projects to advance the technological foundations of AI and its applications in three core areas: scientific discovery, human thriving, and the health of the planet.

“Artificial intelligence is transforming nearly every aspect of human endeavor. MIT’s leadership in AI is greatly enriched through collaborations with leading academic institutions in the U.S. and around the world,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “Our collaboration with MBZUAI reflects a shared commitment to advancing AI in ways that are responsible, inclusive, and globally impactful. Together, we can explore new horizons in AI and bring broad benefits to society.”

“This agreement will unite the efforts of researchers at two world-class institutions to advance frontier AI research across scientific discovery, human thriving, and the health of the planet. By combining MBZUAI’s focus on foundational models and real-world deployment with MIT’s depth in computing and interdisciplinary innovation, we are creating a transcontinental bridge for discovery. Together, we will not only expand the boundaries of AI science, but also ensure that these breakthroughs are pursued responsibly and applied where they matter most — improving human health, enabling intelligent robotics, and driving sustainable AI at scale,” says Eric Xing, president and university professor at MBZUAI.

Each institution has appointed an academic director to oversee the program on its campus. At MIT, Philip Isola, the Class of 1948 Career Development Professor in the Department of Electrical Engineering and Computer Science, will serve as program lead. At MBZUAI, Le Song, professor of machine learning, will take on the role.

Supported by MBZUAI — the first university dedicated entirely to advancing science through AI, and based in Abu Dhabi, U.A.E. — the collaboration will fund a number of joint research projects per year. The findings will be openly publishable, and each project will be led by a principal investigator from MIT and one from MBZUAI, with project selections made by a steering committee composed of representatives from both institutions.

MIT associate professor of physics Riccardo Comin has been selected as 2025 Experimental Physics Investigator by the Gordon and Betty Moore Foundation. Two MIT physics alumni — Gyu-Boong Jo PhD ’10 of Rice University, and Ben Jones PhD ’15 of the University of Texas at Arlington — were also among this year’s cohort of 22 honorees.

The prestigious Experimental Physics Investigators (EPI) Initiative recognizes mid-career scientists advancing the frontiers of experimental physics. Each award provides $1.3 million over five years to accelerate breakthroughs and strengthen the experimental physics community.

At MIT, Comin investigates magnetoelectric multiferroics by engineering interfaces between two-dimensional materials and three-dimensional oxide thin films. His research aims to overcome long-standing limitations in spin-charge coupling by moving beyond epitaxial constraints, enabling new interfacial phases and coupling mechanisms. In these systems, Comin’s team explores the coexistence and proximity of magnetic and ferroelectric order, with a focus on achieving strong magnetoelectric coupling. This approach opens new pathways for designing tunable multiferroic systems unconstrained by traditional synthesis methods.

Comin’s research expands the frontier of multiferroics by demonstrating stacking-controlled magnetoelectric coupling at 2D–3D interfaces. This approach enables exploration of fundamental physics in a versatile materials platform and opens new possibilities for spintronics, sensing, and data storage. By removing constraints of epitaxial growth, Comin’s work lays the foundation for microelectronic and spintronic devices with novel functionalities driven by interfacial control of spin and polarization.

Comin’s project, Interfacial MAGnetoElectrics (I-MAGinE), aims to study a new class of artificial magnetoelectric multiferroics at the interfaces between ferroic materials from 2D van der Waals systems and 3D oxide thin films. The team aims to identify and understand novel magnetoelectric effects to demonstrate the viability of stacking-controlled interfacial magnetoelectric coupling. This research could lead to significant contributions in multiferroics, and could pave the way for innovative, energy-efficient storage devices.

“This research has the potential to make significant contributions to the field of multiferroics by demonstrating the viability of stacking-controlled interfacial magnetoelectric coupling,” according to Comin’s proposal. “The findings could pave the way for future applications in spintronics, data storage, and sensing. It offers a significant opportunity to explore fundamental physics questions in a novel materials platform, while laying the ground for future technological applications, including microelectronic and spintronic devices with new functionalities.”

Comin’s group has extensive experience in researching 2D and 3D ferroic materials and electronically ordered oxide thin films, as well as ultrathin van der Waals magnets, ferroelectrics, and multiferroics. Their lab is equipped with state-of-the-art tools for material synthesis, including bulk crystal growth of van der Waals materials and pulsed laser deposition targets, along with comprehensive fabrication and characterization capabilities. Their expertise in magneto-optical probes and advanced magnetic X-ray techniques promises to enable in-depth studies of electronic and magnetic structures, specifically spin-charge coupling, in order to contribute significantly to understanding spin-charge coupling in magnetochiral materials.

The coexistence of ferroelectricity and ferromagnetism in a single material, known as multiferroicity, is rare, and strong spin-charge coupling is even rarer due to fundamental chemical and electronic structure incompatibilities.

The few known bulk multiferroics with strong magnetoelectric coupling generally rely on inversion symmetry-breaking spin arrangements, which only emerge at low temperatures, limiting practical applications. While interfacial magnetoelectric multiferroics offer an alternative, achieving efficient spin-charge coupling often requires stringent conditions like epitaxial growth and lattice matching, which limit material combinations. This research proposes to overcome these limitations by using non-epitaxial interfaces of 2D van der Waals materials and 3D oxide thin films.

Unique features of this approach include leveraging the versatility of 2D ferroics for seamless transfer onto any substrate, eliminating lattice matching requirements, and exploring new classes of interfacial magnetoelectric effects unconstrained by traditional thin-film synthesis limitations.

Launched in 2018, the Moore Foundation’s EPI Initiative cultivates collaborative research environments and provides research support to promote the discovery of new ideas and emphasize community building.

“We have seen numerous new connections form and new research directions pursued by both individuals and groups based on conversations at these gatherings,” says Catherine Mader, program officer for the initiative.

The Gordon and Betty Moore Foundation was established to create positive outcomes for future generations. In pursuit of that vision, it advances scientific discovery, environmental conservation, and the special character of the San Francisco Bay Area.

Ammonia is one of the most widely produced chemicals in the world, used mostly as fertilizer, but also for the production of some plastics, textiles, and other applications. Its production, through processes that require high heat and pressure, accounts for up to 20 percent of all the greenhouse gases from the entire chemical industry, so efforts have been underway worldwide to find ways to reduce those emissions.

Now, researchers at MIT have come up with a clever way of combining two different methods of producing the compound that minimizes waste products, that, when combined with some other simple upgrades, could reduce the greenhouse emissions from production by as much as 63 percent, compared to the leading “low-emissions” approach being used today.

The new approach is described in the journal Energy & Fuels, in a paper by MIT Energy Initiative (MITEI) Director William H. Green, graduate student Sayandeep Biswas, MITEI Director of Research Randall Field, and two others.

“Ammonia has the most carbon dioxide emissions of any kind of chemical,” says Green, who is the Hoyt C. Hottel Professor in Chemical Engineering. “It’s a very important chemical,” he says, because its use as a fertilizer is crucial to being able to feed the world’s population.

Until late in the 19th century, the most widely used source of nitrogen fertilizer was mined deposits of bat or bird guano, mostly from Chile, but that source was beginning to run out, and there were predictions that the world would soon be running short of food to sustain the population. But then a new chemical process, called the Haber-Bosch process after its inventors, made it possible to make ammonia out of nitrogen from the air and hydrogen, which was mostly derived from methane. But both the burning of fossil fuels to provide the needed heat and the use of methane to make the hydrogen led to massive climate-warming emissions from the process.

To address this, two newer variations of ammonia production have been developed: so-called “blue ammonia,” where the greenhouse gases are captured right at the factory and then sequestered deep underground, and “green ammonia,” produced by a different chemical pathway, using electricity instead of fossil fuels to hydrolyze water to make hydrogen.

Blue ammonia is already beginning to be used, with a few plants operating now in Louisiana, Green says, and the ammonia mostly being shipped to Japan, “so that’s already kind of commercial.” Other parts of the world are starting to use green ammonia, especially in places that have lots of hydropower, solar, or wind to provide inexpensive electricity, including a giant plant now under construction in Saudi Arabia.

But in most places, both blue and green ammonia are still more expensive than the traditional fossil-fuel-based version, so many teams around the world have been working on ways to cut these costs as much as possible so that the difference is small enough to be made up through tax subsidies or other incentives.

The problem is growing, because as the population grows, and as wealth increases, there will be ever-increasing demands for nitrogen fertilizer. At the same time, ammonia is a promising substitute fuel to power hard-to-decarbonize transportation such as cargo ships and heavy trucks, which could lead to even greater needs for the chemical.

“It definitely works” as a transportation fuel, by powering fuel cells that have been demonstrated for use by everything from drones to barges and tugboats and trucks, Green says. “People think that the most likely market of that type would be for shipping,” he says, “because the downside of ammonia is it’s toxic and it’s smelly, and that makes it slightly dangerous to handle and to ship around.” So its best uses may be where it’s used in high volume and in relatively remote locations, like the high seas. In fact, the International Maritime Organization will soon be voting on new rules that might give a strong boost to the ammonia alternative for shipping.

The key to the new proposed system is to combine the two existing approaches in one facility, with a blue ammonia factory next to a green ammonia factory. The process of generating hydrogen for the green ammonia plant leaves a lot of leftover oxygen that just gets vented to the air. Blue ammonia, on the other hand, uses a process called autothermal reforming that requires a source of pure oxygen, so if there’s a green ammonia plant next door, it can use that excess oxygen.

“Putting them next to each other turns out to have significant economic value,” Green says. This synergy could help hybrid “blue-green ammonia” facilities serve as an important bridge toward a future where eventually green ammonia, the cleanest version, could finally dominate. But that future is likely decades away, Green says, so having the combined plants could be an important step along the way.

“It might be a really long time before [green ammonia] is actually attractive” economically, he says. “Right now, it’s nowhere close, except in very special situations.” But the combined plants “could be a really appealing concept, and maybe a good way to start the industry,” because so far only small, standalone demonstration plants of the green process are being built.

“If green or blue ammonia is going to become the new way of making ammonia, you need to find ways to make it relatively affordable in a lot of countries, with whatever resources they’ve got,” he says. This new proposed combination, he says, “looks like a really good idea that can help push things along. Ultimately, there’s got to be a lot of green ammonia plants in a lot of places,” and starting out with the combined plants, which could be more affordable now, could help to make that happen. The team has filed for a patent on the process.

Although the team did a detailed study of both the technology and the economics that show the system has great promise, Green points out that “no one has ever built one. We did the analysis, it looks good, but surely when people build the first one, they’ll find funny little things that need some attention,” such as details of how to start up or shut down the process. “I would say there’s plenty of additional work to do to make it a real industry.” But the results of this study, which shows the costs to be much more affordable than existing blue or green plants in isolation, “definitely encourages the possibility of people making the big investments that would be needed to really make this industry feasible.”

This proposed integration of the two methods “improves efficiency, reduces greenhouse gas emissions, and lowers overall cost,” says Kevin van Geem, a professor in the Center for Sustainable Chemistry at Ghent University, who was not associated with this research. “The analysis is rigorous, with validated process models, transparent assumptions, and comparisons to literature benchmarks. By combining techno-economic analysis with emissions accounting, the work provides a credible and balanced view of the trade-offs.”

He adds that, “given the scale of global ammonia production, such a reduction could have a highly impactful effect on decarbonizing one of the most emissions-intensive chemical industries.”

The research team also included MIT postdoc Angiras Menon and MITEI research lead Guiyan Zang. The work was supported by IHI Japan through the MIT Energy Initiative and the Martin Family Society of Fellows for Sustainability. 

Ammonia is one of the most widely produced chemicals in the world, used mostly as fertilizer, but also for the production of some plastics, textiles, and other applications. Its production, through processes that require high heat and pressure, accounts for up to 20 percent of all the greenhouse gases from the entire chemical industry, so efforts have been underway worldwide to find ways to reduce those emissions.

Now, researchers at MIT have come up with a clever way of combining two different methods of producing the compound that minimizes waste products, that, when combined with some other simple upgrades, could reduce the greenhouse emissions from production by as much as 63 percent, compared to the leading “low-emissions” approach being used today.

The new approach is described in the journal Energy & Fuels, in a paper by MIT Energy Initiative (MITEI) Director William H. Green, graduate student Sayandeep Biswas, MITEI Director of Research Randall Field, and two others.

“Ammonia has the most carbon dioxide emissions of any kind of chemical,” says Green, who is the Hoyt C. Hottel Professor in Chemical Engineering. “It’s a very important chemical,” he says, because its use as a fertilizer is crucial to being able to feed the world’s population.

Until late in the 19th century, the most widely used source of nitrogen fertilizer was mined deposits of bat or bird guano, mostly from Chile, but that source was beginning to run out, and there were predictions that the world would soon be running short of food to sustain the population. But then a new chemical process, called the Haber-Bosch process after its inventors, made it possible to make ammonia out of nitrogen from the air and hydrogen, which was mostly derived from methane. But both the burning of fossil fuels to provide the needed heat and the use of methane to make the hydrogen led to massive climate-warming emissions from the process.

To address this, two newer variations of ammonia production have been developed: so-called “blue ammonia,” where the greenhouse gases are captured right at the factory and then sequestered deep underground, and “green ammonia,” produced by a different chemical pathway, using electricity instead of fossil fuels to hydrolyze water to make hydrogen.

Blue ammonia is already beginning to be used, with a few plants operating now in Louisiana, Green says, and the ammonia mostly being shipped to Japan, “so that’s already kind of commercial.” Other parts of the world are starting to use green ammonia, especially in places that have lots of hydropower, solar, or wind to provide inexpensive electricity, including a giant plant now under construction in Saudi Arabia.

But in most places, both blue and green ammonia are still more expensive than the traditional fossil-fuel-based version, so many teams around the world have been working on ways to cut these costs as much as possible so that the difference is small enough to be made up through tax subsidies or other incentives.

The problem is growing, because as the population grows, and as wealth increases, there will be ever-increasing demands for nitrogen fertilizer. At the same time, ammonia is a promising substitute fuel to power hard-to-decarbonize transportation such as cargo ships and heavy trucks, which could lead to even greater needs for the chemical.

“It definitely works” as a transportation fuel, by powering fuel cells that have been demonstrated for use by everything from drones to barges and tugboats and trucks, Green says. “People think that the most likely market of that type would be for shipping,” he says, “because the downside of ammonia is it’s toxic and it’s smelly, and that makes it slightly dangerous to handle and to ship around.” So its best uses may be where it’s used in high volume and in relatively remote locations, like the high seas. In fact, the International Maritime Organization will soon be voting on new rules that might give a strong boost to the ammonia alternative for shipping.

The key to the new proposed system is to combine the two existing approaches in one facility, with a blue ammonia factory next to a green ammonia factory. The process of generating hydrogen for the green ammonia plant leaves a lot of leftover oxygen that just gets vented to the air. Blue ammonia, on the other hand, uses a process called autothermal reforming that requires a source of pure oxygen, so if there’s a green ammonia plant next door, it can use that excess oxygen.

“Putting them next to each other turns out to have significant economic value,” Green says. This synergy could help hybrid “blue-green ammonia” facilities serve as an important bridge toward a future where eventually green ammonia, the cleanest version, could finally dominate. But that future is likely decades away, Green says, so having the combined plants could be an important step along the way.

“It might be a really long time before [green ammonia] is actually attractive” economically, he says. “Right now, it’s nowhere close, except in very special situations.” But the combined plants “could be a really appealing concept, and maybe a good way to start the industry,” because so far only small, standalone demonstration plants of the green process are being built.

“If green or blue ammonia is going to become the new way of making ammonia, you need to find ways to make it relatively affordable in a lot of countries, with whatever resources they’ve got,” he says. This new proposed combination, he says, “looks like a really good idea that can help push things along. Ultimately, there’s got to be a lot of green ammonia plants in a lot of places,” and starting out with the combined plants, which could be more affordable now, could help to make that happen. The team has filed for a patent on the process.

Although the team did a detailed study of both the technology and the economics that show the system has great promise, Green points out that “no one has ever built one. We did the analysis, it looks good, but surely when people build the first one, they’ll find funny little things that need some attention,” such as details of how to start up or shut down the process. “I would say there’s plenty of additional work to do to make it a real industry.” But the results of this study, which shows the costs to be much more affordable than existing blue or green plants in isolation, “definitely encourages the possibility of people making the big investments that would be needed to really make this industry feasible.”

This proposed integration of the two methods “improves efficiency, reduces greenhouse gas emissions, and lowers overall cost,” says Kevin van Geem, a professor in the Center for Sustainable Chemistry at Ghent University, who was not associated with this research. “The analysis is rigorous, with validated process models, transparent assumptions, and comparisons to literature benchmarks. By combining techno-economic analysis with emissions accounting, the work provides a credible and balanced view of the trade-offs.”

He adds that, “given the scale of global ammonia production, such a reduction could have a highly impactful effect on decarbonizing one of the most emissions-intensive chemical industries.”

The research team also included MIT postdoc Angiras Menon and MITEI research lead Guiyan Zang. The work was supported by IHI Japan through the MIT Energy Initiative and the Martin Family Society of Fellows for Sustainability. 

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of this versatility? Billions, or even trillions, of textual data points across the internet.

Those data aren’t enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task. Collecting these demonstrations on real robots is time-consuming and not perfectly repeatable, so engineers have created training data by generating simulations with AI (which don’t often reflect real-world physics), or tediously handcrafting each digital environment from scratch.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Toyota Research Institute may have found a way to create the diverse, realistic training grounds robots need. Their “steerable scene generation” approach creates digital scenes of things like kitchens, living rooms, and restaurants that engineers can use to simulate lots of real-world interactions and scenarios. Trained on over 44 million 3D rooms filled with models of objects such as tables and plates, the tool places existing assets in new scenes, then refines each one into a physically accurate, lifelike environment.

Steerable scene generation creates these 3D worlds by “steering” a diffusion model — an AI system that generates a visual from random noise — toward a scene you’d find in everyday life. The researchers used this generative system to “in-paint” an environment, filling in particular elements throughout the scene. You can imagine a blank canvas suddenly turning into a kitchen scattered with 3D objects, which are gradually rearranged into a scene that imitates real-world physics. For example, the system ensures that a fork doesn’t pass through a bowl on a table — a common glitch in 3D graphics known as “clipping,” where models overlap or intersect.

How exactly steerable scene generation guides its creation toward realism, however, depends on the strategy you choose. Its main strategy is “Monte Carlo tree search” (MCTS), where the model creates a series of alternative scenes, filling them out in different ways toward a particular objective (like making a scene more physically realistic, or including as many edible items as possible). It’s used by the AI program AlphaGo to beat human opponents in Go (a game similar to chess), as the system considers potential sequences of moves before choosing the most advantageous one.

“We are the first to apply MCTS to scene generation by framing the scene generation task as a sequential decision-making process,” says MIT Department of Electrical Engineering and Computer Science (EECS) PhD student Nicholas Pfaff, who is a CSAIL researcher and a lead author on a paper presenting the work. “We keep building on top of partial scenes to produce better or more desired scenes over time. As a result, MCTS creates scenes that are more complex than what the diffusion model was trained on.”

In one particularly telling experiment, MCTS added the maximum number of objects to a simple restaurant scene. It featured as many as 34 items on a table, including massive stacks of dim sum dishes, after training on scenes with only 17 objects on average.

Steerable scene generation also allows you to generate diverse training scenarios via reinforcement learning — essentially, teaching a diffusion model to fulfill an objective by trial-and-error. After you train on the initial data, your system undergoes a second training stage, where you outline a reward (basically, a desired outcome with a score indicating how close you are to that goal). The model automatically learns to create scenes with higher scores, often producing scenarios that are quite different from those it was trained on.

Users can also prompt the system directly by typing in specific visual descriptions (like “a kitchen with four apples and a bowl on the table”). Then, steerable scene generation can bring your requests to life with precision. For example, the tool accurately followed users’ prompts at rates of 98 percent when building scenes of pantry shelves, and 86 percent for messy breakfast tables. Both marks are at least a 10 percent improvement over comparable methods like “MiDiffusion” and “DiffuScene.”

The system can also complete specific scenes via prompting or light directions (like “come up with a different scene arrangement using the same objects”). You could ask it to place apples on several plates on a kitchen table, for instance, or put board games and books on a shelf. It’s essentially “filling in the blank” by slotting items in empty spaces, but preserving the rest of a scene.

According to the researchers, the strength of their project lies in its ability to create many scenes that roboticists can actually use. “A key insight from our findings is that it’s OK for the scenes we pre-trained on to not exactly resemble the scenes that we actually want,” says Pfaff. “Using our steering methods, we can move beyond that broad distribution and sample from a ‘better’ one. In other words, generating the diverse, realistic, and task-aligned scenes that we actually want to train our robots in.”

Such vast scenes became the testing grounds where they could record a virtual robot interacting with different items. The machine carefully placed forks and knives into a cutlery holder, for instance, and rearranged bread onto plates in various 3D settings. Each simulation appeared fluid and realistic, resembling the real-world, adaptable robots steerable scene generation could help train, one day.

While the system could be an encouraging path forward in generating lots of diverse training data for robots, the researchers say their work is more of a proof of concept. In the future, they’d like to use generative AI to create entirely new objects and scenes, instead of using a fixed library of assets. They also plan to incorporate articulated objects that the robot could open or twist (like cabinets or jars filled with food) to make the scenes even more interactive.

To make their virtual environments even more realistic, Pfaff and his colleagues may incorporate real-world objects by using a library of objects and scenes pulled from images on the internet and using their previous work on “Scalable Real2Sim.” By expanding how diverse and lifelike AI-constructed robot testing grounds can be, the team hopes to build a community of users that’ll create lots of data, which could then be used as a massive dataset to teach dexterous robots different skills.

“Today, creating realistic scenes for simulation can be quite a challenging endeavor; procedural generation can readily produce a large number of scenes, but they likely won’t be representative of the environments the robot would encounter in the real world. Manually creating bespoke scenes is both time-consuming and expensive,” says Jeremy Binagia, an applied scientist at Amazon Robotics who wasn’t involved in the paper. “Steerable scene generation offers a better approach: train a generative model on a large collection of pre-existing scenes and adapt it (using a strategy such as reinforcement learning) to specific downstream applications. Compared to previous works that leverage an off-the-shelf vision-language model or focus just on arranging objects in a 2D grid, this approach guarantees physical feasibility and considers full 3D translation and rotation, enabling the generation of much more interesting scenes.”

“Steerable scene generation with post training and inference-time search provides a novel and efficient framework for automating scene generation at scale,” says Toyota Research Institute roboticist Rick Cory SM ’08, PhD ’10, who also wasn’t involved in the paper. “Moreover, it can generate ‘never-before-seen’ scenes that are deemed important for downstream tasks. In the future, combining this framework with vast internet data could unlock an important milestone towards efficient training of robots for deployment in the real world.”

Pfaff wrote the paper with senior author Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT; a senior vice president of large behavior models at the Toyota Research Institute; and CSAIL principal investigator. Other authors were Toyota Research Institute robotics researcher Hongkai Dai SM ’12, PhD ’16; team lead and Senior Research Scientist Sergey Zakharov; and Carnegie Mellon University PhD student Shun Iwase. Their work was supported, in part, by Amazon and the Toyota Research Institute. The researchers presented their work at the Conference on Robot Learning (CoRL) in September.

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of this versatility? Billions, or even trillions, of textual data points across the internet.

Those data aren’t enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task. Collecting these demonstrations on real robots is time-consuming and not perfectly repeatable, so engineers have created training data by generating simulations with AI (which don’t often reflect real-world physics), or tediously handcrafting each digital environment from scratch.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Toyota Research Institute may have found a way to create the diverse, realistic training grounds robots need. Their “steerable scene generation” approach creates digital scenes of things like kitchens, living rooms, and restaurants that engineers can use to simulate lots of real-world interactions and scenarios. Trained on over 44 million 3D rooms filled with models of objects such as tables and plates, the tool places existing assets in new scenes, then refines each one into a physically accurate, lifelike environment.

Steerable scene generation creates these 3D worlds by “steering” a diffusion model — an AI system that generates a visual from random noise — toward a scene you’d find in everyday life. The researchers used this generative system to “in-paint” an environment, filling in particular elements throughout the scene. You can imagine a blank canvas suddenly turning into a kitchen scattered with 3D objects, which are gradually rearranged into a scene that imitates real-world physics. For example, the system ensures that a fork doesn’t pass through a bowl on a table — a common glitch in 3D graphics known as “clipping,” where models overlap or intersect.

How exactly steerable scene generation guides its creation toward realism, however, depends on the strategy you choose. Its main strategy is “Monte Carlo tree search” (MCTS), where the model creates a series of alternative scenes, filling them out in different ways toward a particular objective (like making a scene more physically realistic, or including as many edible items as possible). It’s used by the AI program AlphaGo to beat human opponents in Go (a game similar to chess), as the system considers potential sequences of moves before choosing the most advantageous one.

“We are the first to apply MCTS to scene generation by framing the scene generation task as a sequential decision-making process,” says MIT Department of Electrical Engineering and Computer Science (EECS) PhD student Nicholas Pfaff, who is a CSAIL researcher and a lead author on a paper presenting the work. “We keep building on top of partial scenes to produce better or more desired scenes over time. As a result, MCTS creates scenes that are more complex than what the diffusion model was trained on.”

In one particularly telling experiment, MCTS added the maximum number of objects to a simple restaurant scene. It featured as many as 34 items on a table, including massive stacks of dim sum dishes, after training on scenes with only 17 objects on average.

Steerable scene generation also allows you to generate diverse training scenarios via reinforcement learning — essentially, teaching a diffusion model to fulfill an objective by trial-and-error. After you train on the initial data, your system undergoes a second training stage, where you outline a reward (basically, a desired outcome with a score indicating how close you are to that goal). The model automatically learns to create scenes with higher scores, often producing scenarios that are quite different from those it was trained on.

Users can also prompt the system directly by typing in specific visual descriptions (like “a kitchen with four apples and a bowl on the table”). Then, steerable scene generation can bring your requests to life with precision. For example, the tool accurately followed users’ prompts at rates of 98 percent when building scenes of pantry shelves, and 86 percent for messy breakfast tables. Both marks are at least a 10 percent improvement over comparable methods like “MiDiffusion” and “DiffuScene.”

The system can also complete specific scenes via prompting or light directions (like “come up with a different scene arrangement using the same objects”). You could ask it to place apples on several plates on a kitchen table, for instance, or put board games and books on a shelf. It’s essentially “filling in the blank” by slotting items in empty spaces, but preserving the rest of a scene.

According to the researchers, the strength of their project lies in its ability to create many scenes that roboticists can actually use. “A key insight from our findings is that it’s OK for the scenes we pre-trained on to not exactly resemble the scenes that we actually want,” says Pfaff. “Using our steering methods, we can move beyond that broad distribution and sample from a ‘better’ one. In other words, generating the diverse, realistic, and task-aligned scenes that we actually want to train our robots in.”

Such vast scenes became the testing grounds where they could record a virtual robot interacting with different items. The machine carefully placed forks and knives into a cutlery holder, for instance, and rearranged bread onto plates in various 3D settings. Each simulation appeared fluid and realistic, resembling the real-world, adaptable robots steerable scene generation could help train, one day.

While the system could be an encouraging path forward in generating lots of diverse training data for robots, the researchers say their work is more of a proof of concept. In the future, they’d like to use generative AI to create entirely new objects and scenes, instead of using a fixed library of assets. They also plan to incorporate articulated objects that the robot could open or twist (like cabinets or jars filled with food) to make the scenes even more interactive.

To make their virtual environments even more realistic, Pfaff and his colleagues may incorporate real-world objects by using a library of objects and scenes pulled from images on the internet and using their previous work on “Scalable Real2Sim.” By expanding how diverse and lifelike AI-constructed robot testing grounds can be, the team hopes to build a community of users that’ll create lots of data, which could then be used as a massive dataset to teach dexterous robots different skills.

“Today, creating realistic scenes for simulation can be quite a challenging endeavor; procedural generation can readily produce a large number of scenes, but they likely won’t be representative of the environments the robot would encounter in the real world. Manually creating bespoke scenes is both time-consuming and expensive,” says Jeremy Binagia, an applied scientist at Amazon Robotics who wasn’t involved in the paper. “Steerable scene generation offers a better approach: train a generative model on a large collection of pre-existing scenes and adapt it (using a strategy such as reinforcement learning) to specific downstream applications. Compared to previous works that leverage an off-the-shelf vision-language model or focus just on arranging objects in a 2D grid, this approach guarantees physical feasibility and considers full 3D translation and rotation, enabling the generation of much more interesting scenes.”

“Steerable scene generation with post training and inference-time search provides a novel and efficient framework for automating scene generation at scale,” says Toyota Research Institute roboticist Rick Cory SM ’08, PhD ’10, who also wasn’t involved in the paper. “Moreover, it can generate ‘never-before-seen’ scenes that are deemed important for downstream tasks. In the future, combining this framework with vast internet data could unlock an important milestone towards efficient training of robots for deployment in the real world.”

Pfaff wrote the paper with senior author Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT; a senior vice president of large behavior models at the Toyota Research Institute; and CSAIL principal investigator. Other authors were Toyota Research Institute robotics researcher Hongkai Dai SM ’12, PhD ’16; team lead and Senior Research Scientist Sergey Zakharov; and Carnegie Mellon University PhD student Shun Iwase. Their work was supported, in part, by Amazon and the Toyota Research Institute. The researchers presented their work at the Conference on Robot Learning (CoRL) in September.

Every time you check the time on your phone, make an online transaction, or use a navigation app, you are depending on the precision of atomic clocks.

An atomic clock keeps time by relying on the “ticks” of atoms as they naturally oscillate at rock-steady frequencies. Today’s atomic clocks operate by tracking cesium atoms, which tick over 10 billion times per second. Each of those ticks is precisely tracked using lasers that oscillate in sync, at microwave frequencies.

Scientists are developing next-generation atomic clocks that rely on even faster-ticking atoms such as ytterbium, which can be tracked with lasers at higher, optical frequencies. If they can be kept stable, optical atomic clocks could track even finer intervals of time, up to 100 trillion times per second.

Now, MIT physicists have found a way to improve the stability of optical atomic clocks, by reducing “quantum noise” — a fundamental measurement limitation due to the effects of quantum mechanics, which obscures the atoms’ pure oscillations. In addition, the team discovered that an effect of a clock’s laser on the atoms, previously considered irrelevant, can be used to further stabilize the laser.

The researchers developed a method to harness a laser-induced “global phase” in ytterbium atoms, and have boosted this effect with a quantum-amplification technique. The new approach doubles the precision of an optical atomic clock, enabling it to discern twice as many ticks per second compared to the same setup without the new method. What’s more, they anticipate that the precision of the method should increase steadily with the number of atoms in an atomic clock.

The researchers detail the method, which they call global phase spectroscopy, in a study appearing today in the journal Nature. They envision that the clock-stabilizing technique could one day enable portable optical atomic clocks that can be transported to various locations to measure all manner of phenomena.

“With these clocks, people are trying to detect dark matter and dark energy, and test whether there really are just four fundamental forces, and even to see if these clocks can predict earthquakes,” says study author Vladan Vuletić, the Lester Wolfe Professor of Physics at MIT. “We think our method can help make these clocks transportable and deployable to where they’re needed.”

The paper’s co-authors are Leon Zaporski, Qi Liu, Gustavo Velez, Matthew Radzihovsky, Zeyang Li, Simone Colombo, and Edwin Pedrozo-Peñafiel, who are members of the MIT-Harvard Center for Ultracold Atoms and the MIT Research Laboratory of Electronics.

Ticking time

In 2020, Vuletić and his colleagues demonstrated that an atomic clock could be made more precise by quantumly entangling the clock’s atoms. Quantum entanglement is a phenomenon by which particles can be made to behave in a collective, highly correlated manner. When atoms are quantumly entangled, they redistribute any noise, or uncertainty in measuring the atoms’ oscillations, in a way that reveals a clearer, more measurable “tick.”

In their previous work, the team induced quantum entanglement among several hundred ytterbium atoms that they first cooled and trapped in a cavity formed by two curved mirrors. They sent a laser into the cavity, which bounced thousands of times between the mirrors, interacting with the atoms and causing the ensemble to entangle. They were able to show that quantum entanglement could improve the precision of existing atomic clocks by essentially reducing the noise, or uncertainty between the laser’s and atoms’ tick rates.

At the time, however, they were limited by the ticking instability of the clock’s laser. In 2022, the same team derived a way to further amplify the difference in laser versus atom tick rates with “time reversal” — a trick that relies on entangling and de-entangling the atoms to boost the signal acquired in between.

However, in that work the team was still using traditional microwaves, which oscillate at much lower frequencies than the optical frequency standards ytterbium atoms can provide. It was as if they had painstakingly lifted a film of dust off a painting, only to then photograph it with a low-resolution camera.

“When you have atoms that tick 100 trillion times per second, that’s 10,000 times faster than the frequency of microwaves,” Vuletić says. “We didn’t know at the time how to apply these methods to higher-frequency optical clocks that are much harder to keep stable.”

About phase

In their new study, the team has found a way to apply their previously developed approach of time reversal to optical atomic clocks. They then sent in a laser that oscillates near the optical frequency of the entangled atoms.

“The laser ultimately inherits the ticking of the atoms,” says first author Zaporski. “But in order for this inheritance to hold for a long time, the laser has to be quite stable.”

The researchers found they were able to improve the stability of an optical atomic clock by taking advantage of a phenomenon that scientists had assumed was inconsequential to the operation. They realized that when light is sent through entangled atoms, the interaction can cause the atoms to jump up in energy, then settle back down into their original energy state and still carry the memory about their round trip.

“One might think we’ve done nothing,” Vuletić says. “You get this global phase of the atoms, which is usually considered irrelevant. But this global phase contains information about the laser frequency.”

In other words, they realized that the laser was inducing a measurable change in the atoms, despite bringing them back to the original energy state, and that the magnitude of this change depends on the laser’s frequency.

“Ultimately, we are looking for the difference of laser frequency and the atomic transition frequency,” explains co-author Liu. “When that difference is small, it gets drowned by quantum noise. Our method amplifies this difference above this quantum noise.”

In their experiments, the team applied this new approach and found that through entanglement they were able to double the precision of their optical atomic clock.

“We saw that we can now resolve nearly twice as small a difference in the optical frequency or, the clock ticking frequency, without running into the quantum noise limit,” Zaporski says. “Although it’s a hard problem in general to run atomic clocks, the technical benefits of our method it will make it easier, and we think this can enable stable, transportable atomic clocks.”

This research was supported, in part, by the U.S. Office of Naval Research, the National Science Foundation, the U.S. Defense Advanced Research Projects Agency, the U.S. Department of Energy, the U.S. Office of Science, the National Quantum Information Science Research Centers, and the Quantum Systems Accelerator.

• The Straits Times, 3 October 2025, The Big Story, pA7

For decades, it’s been known that subtle chemical patterns exist in metal alloys, but researchers thought they were too minor to matter — or that they got erased during manufacturing. However, recent studies have shown that in laboratory settings, these patterns can change a metal’s properties, including its mechanical strength, durability, heat capacity, radiation tolerance, and more.

Now, researchers at MIT have found that these chemical patterns also exist in conventionally manufactured metals. The surprising finding revealed a new physical phenomenon that explains the persistent patterns.

In a paper published in Nature Communications today, the researchers describe how they tracked the patterns and discovered the physics that explains them. The authors also developed a simple model to predict chemical patterns in metals, and they show how engineers could use the model to tune the effect of such patterns on metallic properties, for use in aerospace, semiconductors, nuclear reactors, and more.

“The conclusion is: You can never completely randomize the atoms in a metal. It doesn’t matter how you process it,” says Rodrigo Freitas, the TDK Assistant Professor in the Department of Materials Science and Engineering. “This is the first paper showing these non-equilibrium states that are retained in the metal. Right now, this chemical order is not something we’re controlling for or paying attention to when we manufacture metals.”

For Freitas, an early-career researcher, the findings offer vindication for exploring a crowded field that he says few believed would lead to unique or broadly impactful results. He credits the U.S. Air Force Office of Scientific Research, which supported the work through their Young Investigator Program. He also credits the collaborative effort that enabled the paper, which features three MIT PhD students as co-first authors: Mahmudul Islam, Yifan Cao, and Killian Sheriff.

“There was the question of whether I should even be tackling this specific problem because people have been working on it for a long time,” Freitas says. “But the more I learned about it, the more I saw researchers were thinking about this in idealized laboratory scenarios. We wanted to perform simulations that were as realistic as possible to reproduce these manufacturing processes with high fidelity. My favorite part of this project is how non-intuitive the findings are. The fact that you cannot completely mix something together, people didn’t see that coming.”

From surprises to theories

Freitas’ research team began with a practical question: How fast do chemical elements mix during metal processing? Conventional wisdom held that there’s a point where the chemical composition of metals becomes completely uniform from mixing during manufacturing. By finding that point, the researchers thought they could develop a simple way to design alloys with different levels of atomic order, also known as short-range order.

The researchers used machine-learning techniques to track millions of atoms as they moved and rearranged themselves under conditions that mimicked metal processing.

“The first thing we did was to deform a piece of metal,” Freitas explains. “That’s a common step during manufacturing: You roll the metal and deform it and heat it up again and deform it a little more, so it develops the structure you want. We did that and we tracked chemical order. The thought was as you deform the material, its chemical bonds are broken and that randomizes the system. These violent manufacturing processes essentially shuffle the atoms.”

The researchers hit a snag during the mixing process: The alloys never reached a fully random state. That was a surprise, because no known physical mechanism could explain the result.

“It pointed to a new piece of physics in metals,” the researchers write in the paper. “It was one of those cases where applied research led to a fundamental discovery.”

To uncover the new physics, the researchers developed computational tools, including high-fidelity machine-learning models, to capture atomic interactions, along with new statistical methods that quantify how chemical order changes over time. They then applied these tools in large-scale molecular dynamics simulations to track how atoms rearrange during processing.

The researchers found some standard chemical arrangements in their processed metals, but at higher temperatures than would normally be expected. Even more surprisingly, they found completely new chemical patterns never seen outside of manufacturing processes. This was the first time such patterns were observed. The researchers referred to the patterns as “far-from-equilibrium states.”

The researchers also built a simple model that reproduced key features of the simulations. The model explains how the chemical patterns arise from defects known as dislocations, which are like three-dimensional scribbles within a metal. As the metal is deformed, those scribbles warp, shuffling nearby atoms along the way. Previously, researchers believed that shuffling completely erased order in the metals, but they found that dislocations favor some atomic swaps over others, resulting not in randomness but in subtle patterns that explain their findings.

“These defects have chemical preferences that guide how they move,” Freitas says. “They look for low energy pathways, so given a choice between breaking chemical bonds, they tend to break the weakest bonds, and it’s not completely random. This is very exciting because it’s a non-equilibrium state: It’s not something you’d see naturally occurring in materials. It’s the same way our bodies live in non-equilibrium. The temperature outside is always hotter or colder than our bodies, and we’re maintaining that steady state equilibrium to stay alive. That’s why these states exist in metal: the balance between an internal push toward disorder plus this ordering tendency of breaking certain bonds that are always weaker than others.”

Applying a new theory

The researchers are now exploring how these chemical patterns develop across a wide range of manufacturing conditions. The result is a map that links various metal processing steps to different chemical patterns in metal.

To date, this chemical order and the properties they tune have been largely considered an academic subject. With this map, the researchers hope engineers can begin thinking of these patterns as levers in design that can be pulled during production to get new properties.

“Researchers have been looking at the ways these atomic arrangements change metallic properties — a big one is catalysis,” Freitas says of the process that drives chemical reactions. “Electrochemistry happens at the surface of the metal, and it’s very sensitive to local atomic arrangements. And there have been other properties that you wouldn't think would be influenced by these factors. Radiation damage is another big one. That affects these materials’ performance in nuclear reactors.”

Researchers have already told Freitas the paper could help explain other surprise findings about metallic properties, and he’s excited for the field to move from fundamental research into chemical order to more applied work.

“You can think of areas where you need very optimized alloys like aerospace,” Freitas says. “They care about very specific compositions. Advanced manufacturing now makes it possible to combine metals that normally wouldn’t mix through deformation. Understanding how atoms actually shuffle and mix in those processes is crucial, because it’s the key to gaining strength while still keeping the low density. So, this could be a huge deal for them.”

This work was supported, in part, by the U.S. Air Force Office of Scientific Research, MathWorks, and the MIT-Portugal Program.

One of the newest weapons that scientists have developed against cancer is a type of engineered immune cell known as CAR-NK (natural killer) cells. Similar to CAR-T cells, these cells can be programmed to attack cancer cells.

MIT and Harvard Medical School researchers have now come up with a new way to engineer CAR-NK cells that makes them much less likely to be rejected by the patient’s immune system, which is a common drawback of this type of treatment.

The new advance may also make it easier to develop “off-the-shelf” CAR-NK cells that could be given to patients as soon as they are diagnosed. Traditional approaches to engineering CAR-NK or CAR-T cells usually take several weeks.

“This enables us to do one-step engineering of CAR-NK cells that can avoid rejection by host T cells and other immune cells. And, they kill cancer cells better and they’re safer,” says Jianzhu Chen, an MIT professor of biology, a member of the Koch Institute for Integrative Cancer Research,and one of the senior authors of the study.

In a study of mice with humanized immune systems, the researchers showed that these CAR-NK cells could destroy most cancer cells while evading the host immune system.

Rizwan Romee, an associate professor of medicine at Harvard Medical School and Dana-Farber Cancer Institute, is also a senior author of the paper, which appears today in Nature Communications. The paper’s lead author is Fuguo Liu, a postdoc at the Koch Institute and a research fellow at Dana-Farber.

Evading the immune system

NK cells are a critical part of the body’s natural immune defenses, and their primary responsibility is to locate and kill cancer cells and virus-infected cells. One of their cell-killing strategies, also used by T cells, is a process called degranulation. Through this process, immune cells release a protein called perforin, which can poke holes in another cell to induce cell death.

To create CAR-NK cells to treat cancer patients, doctors first take a blood sample from the patient. NK cells are isolated from the sample and engineered to express a protein called a chimeric antigen receptor (CAR), which can be designed to target specific proteins found on cancer cells.

Then, the cells spend several weeks proliferating until there are enough to transfuse back into the patient. A similar approach is also used to create CAR-T cells. Several CAR-T cell therapies have been approved to treat blood cancers such as lymphoma and leukemia, but CAR-NK treatments are still in clinical trials.

Because it takes so long to grow a population of engineered cells that can be infused into the patient, and those cells may not be as viable as cells that came from a healthy person, researchers are exploring an alternative approach: using NK cells from a healthy donor.

Such cells could be grown in large quantities and would be ready whenever they were needed. However, the drawback to these cells is that the recipient’s immune system may see them as foreign and attack them before they can start killing cancer cells.

In the new study, the MIT team set out to find a way to help NK cells “hide” from a patient’s immune system. Through studies of immune cell interactions, they showed that NK cells could evade a host T-cell response if they did not carry surface proteins called HLA class 1 proteins. These proteins, usually expressed on NK cell surfaces, can trigger T cells to attack if the immune system doesn’t recognize them as “self.”

To take advantage of this, the researchers engineered the cells to express a sequence of siRNA (short interfering RNA) that interferes with the genes for HLA class 1. They also delivered the CAR gene, as well as the gene for either PD-L1 or single-chain HLA-E (SCE). PD-L1 and SCE are proteins that make NK cells more effective by turning up genes that are involved in killing cancer cells.

All of these genes can be carried on a single piece of DNA, known as a construct, making it simple to transform donor NK cells into immune-evasive CAR-NK cells. The researchers used this construct to create CAR-NK cells targeting a protein called CD-19, which is often found on cancerous B cells in lymphoma patients.

NK cells unleashed

The researchers tested these CAR-NK cells in mice with a human-like immune system. These mice were also injected with lymphoma cells.

Mice that received CAR-NK cells with the new construct maintained the NK cell population for at least three weeks, and the NK cells were able to nearly eliminate cancer in those mice. In mice that received either NK cells with no genetic modifications or NK cells with only the CAR gene, the host immune cells attacked the donor NK cells. In these mice, the NK cells died out within two weeks, and the cancer spread unchecked.

The researchers also found that these engineered CAR-NK cells were much less likely to induce cytokine release syndrome — a common side effect of immunotherapy treatments, which can cause life-threatening complications.

Because of CAR-NK cells’ potentially better safety profile, Chen anticipates that they could eventually be used in place of CAR-T cells. For any CAR-NK cells that are now in development to target lymphoma or other types of cancer, it should be possible to adapt them by adding the construct developed in this study, he says.

The researchers now hope to run a clinical trial of this approach, working with colleagues at Dana-Farber. They are also working with a local biotech company to test CAR-NK cells to treat lupus, an autoimmune disorder that causes the immune system to attack healthy tissues and organs.

The research was funded, in part, by Skyline Therapeutics, the Koch Institute Frontier Research Program through the Kathy and Curt Marble Cancer Research Fund and the Elisa Rah (2004, 2006) Memorial Fund, the Claudia Adams Barr Foundation, and the Koch Institute Support (core) Grant from the National Cancer Institute.

By Ms Nguyen Tran Bao Phuong, Research Fellow, and Ms Tran Thi Ngoc My, Research Analyst, both at the Asia Competitiveness Institute, Lee Kuan Yew School of Public Policy at NUS

• The Business Times, 7 October 2025, p18

• Lianhe Zaobao Online, 2 October 2025

• Money 89.3FM, 1 October 2025

• The Straits Times, 2 October 2025, Singapore, pA18

• The Straits Times, 2 October 2025, Sport, pB14

• The Straits Times, 2 October 2025, Life, pC4

• The Straits Times, 2 October 2025, The Big Story, pA8

• Lianhe Zaobao, 7 October 2025, Singapore, p11

• Capital 95.8FM, 30 September 2025

• Hao 96.3FM, 30 September 2025

• 8world Online, 30 September 2025

• The Straits Times, 1 October 2025, Singapore, pA14

• Lianhe Zaobao, 1 October 2025, Singapore, p7

NUS and OceanX have embarked on a 24-day voyage to the Monsoon Rise in the Christmas Island Seamount Province, a largely unexplored seamount chain in the eastern Indian Ocean. This is Singapore’s first deep-sea scientific expedition since signing the United Nations Biodiversity Beyond National Jurisdiction (BBNJ) Agreement.

A concerted effort to explore unchartered waters

On board the state-of-the-art research vessel − the OceanXplorer − 14 NUS researchers are joined by 2 researchers from NTU, 2 from Indonesia, and 1 each from Thailand, Vietnam and Fiji.

On 4 October 2025, a ceremonial send-off on the OceanXplorer at Marina @ Keppel Bay was graced by government leaders, scientists, and partners. Dr Vivian Balakrishnan, Minister for Foreign Affairs; Mr Heng Swee Keat, Chairman of National Research Foundation (NRF); Professor Tan Eng Chye, NUS President; Mr Ray Dalio, Co-Founder of OceanX; and Mr Mark Dalio, Co-Founder and Co-CEO of OceanX, were among the distinguished guests who attended the event.

“This expedition demonstrates Singapore’s commitment to the conservation and sustainable use of marine biodiversity in areas beyond national jurisdiction, especially following the adoption of the BBNJ Agreement in 2023 and its imminent entry into force in January 2026. Such cutting-edge research is critical in building the scientific foundation needed to understand, protect, and preserve marine biodiversity,” said Dr Balakrishnan.

“Singapore is committed to the inclusive implementation of the BBNJ Agreement which taps on the expertise of ASEAN and Small Island Developing States. The collaborative approach will contribute to capacity building, and the fair and equitable sharing of benefits from marine scientific research in the high seas,” added Dr Balakrishnan.

This expedition and the development of deep-sea research capacity in Singapore is supported by a S$6 million grant from the NRF.

“This mission is more than a voyage of discovery — it is a contribution to advancing collaboration and ocean conservation, for a better future of our planet,” said NUS President Prof Tan.

Diving into greater depths

Launched in 2016, OceanX is a nonprofit initiative that combines ocean exploration, science, education and storytelling to uncover the mysteries of the ocean, unlock its potential and inspire greater protection of it. A floating laboratory equipped with remotely operated vehicles and submersibles that can reach maximum depths of 6,000m and 1,000m respectively, the OceanXplorer have since conducted expeditions in the Red Sea, and in the seas around Malaysia, Indonesia and Africa.

“OceanXplorer was designed as a platform to unite exploration, science, media, and education. Partnering with NUS on this mission allows us to expand knowledge of one of the last great unknowns of our planet and inspire people across Asia and the world to protect it,” said Mr Mark Dalio, Founder and Co-CEO of OceanX.

Led by Dr Tan Koh Siang, Chief Expedition Scientist and Head of the Marine Biology and Ecology Laboratory at the NUS Tropical Marine Science Institute (TMSI), the 24-day mission also includes researchers from Vietnam National University, Indonesia’s Diponegoro University and National Research and Innovation Agency, Thailand’s Kasetsart University, and Fiji’s Social Empowerment Education Programme.

“For Singapore and NUS, this expedition is both a scientific breakthrough and a regional milestone. It strengthens our deep-sea research capacity, builds collaboration across ASEAN, and positions Singapore as a hub for international marine science,” said Professor Peter Ng, mission lead from the Lee Kong Chian Natural History Museum (LKCNHM) and TMSI at NUS.

Uncovering seamount secrets

Seamounts are massive underwater mountains with steep sides rising from the seafloor. Often formed by extinct volcanoes, seamounts are known to be teeming with marine life. Their solid surfaces allow corals and sponges to grow, providing small sea creatures with a home, which in turn attracts larger predators. Although there are over 10,000 seamounts globally, these underwater oases remain poorly documented. Establishing a scientific record of the marine life in these deep-sea regions is crucial for shaping conservation and sustainable management strategies.

The expedition aims to characterise the biodiversity over more than 17,000 square kilometres of deep-sea terrain. Specially constructed traps – made of a system of traps within a trap – will be deployed to ensure that captured fish will be separated from their predators. Samples of seawater from different regions will also be collected to study eDNA (environmental DNA) – the trace amount of DNA shed by organisms from their skin, body fluids or waste. Extracting eDNA from water samples for sequencing and genetic analysis allows scientists to detect biodiversity without direct capture.

Specimens discovered from the expedition will be curated at LKCNHM and findings will be published in peer-reviewed and open access journals to support international ocean conservation and management efforts.

“The Indian Ocean remains one of the least studied parts of our planet. This mission gives us an unprecedented opportunity to gather insights that can transform how we understand biodiversity in this region,” said Dr Vincent Pieribone, Co-CEO and Chief Science Officer of OceanX.

In 2018, LKCNHM and TMSI collaborated with the Indonesian Institute of Sciences for a 14-day deep-sea biodiversity expedition. South Java Deep Sea Biodiversity Expedition (SJADES) surveyed 63 sites over a remarkable distance of 2,200 kilometres, yielding over 12,000 specimens from 800 species, including sponges, jellyfish, molluscs, starfish, worms, crabs, prawns and fish. Among these, more than 12 new species were discovered, and over 40 species were new records for Indonesia.

Last year, two NUS students had the privilege to live on board the OceanXplorer in the OceanX Education Young Explorers Programme (YEP) for 5 days, travelling from Jakarta to Bali. Participating in activities like seminars, lab work, job shadowing and even snorkelling, the eye-opening experience ignited their passion for marine biology and deepened their connection and understanding of the ocean.

By Prof Joseph Chinyong Liow, Chairman of the Middle East Institute at NUS

• Berita Harian, 1 October 2025, p9

By Dr Azhar Ibrahim Alwee, Senior Lecturer from the Dept of Malay Studies, Faculty of Arts and Social Sciences at NUS

• Berita Minggu, 5 October 2025, p9

• The Sunday Times, 5 October 2025, Life, pC4 and pC5

• 8world Online, 10 October 2025

• 8world Online, 3 October 2025
• CNA, 3 October 2025
• Money 89.3FM, 3 October 2025
• The Straits Times, 4 October 2025, Singapore, pA18
• Lianhe Zaobao, 4 October 2025, Singapore, p5

By Mr Carl Skadian, Deputy Director from the Middle East Institute at NUS

• CNA Online, 30 September 2025

Laurent Demanet, MIT professor of applied mathematics, has been appointed co-director of the MIT Center for Computational Science and Engineering (CCSE), effective Sept. 1.

Demanet, who holds a joint appointment in the departments of Mathematics and Earth, Atmospheric and Planetary Sciences — where he previously served as director of the Earth Resources Laboratory — succeeds Youssef Marzouk, who is now serving as the associate dean of the MIT Schwarzman College of Computing.

Joining co-director Nicolas Hadjiconstantinou, the Quentin Berg (1937) Professor of Mechanical Engineering, Demanet will help lead CCSE, supporting students, faculty, and researchers while fostering a vibrant community of innovation and discovery in computational science and engineering (CSE).

“Laurent’s ability to translate concepts of computational science and engineering into understandable, real-world applications is an invaluable asset to CCSE. His interdisciplinary experience is a benefit to the visibility and impact of CSE research and education. I look forward to working with him,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.

“I’m pleased to welcome Laurent into his new role as co-director of CCSE. His work greatly supports the cross-cutting methodology at the heart of the computational science and engineering community. I’m excited for CCSE to have a co-director from the School of Science, and eager to see the center continue to broaden its connections across MIT,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing, department head of Electrical Engineering and Computer Science, and MathWorks Professor.

Established in 2008, CCSE was incorporated into the MIT Schwarzman College of Computing as one of its core academic units in January 2020. An interdisciplinary research and education center dedicated to pioneering applications of computation, CCSE houses faculty, researchers, and students from a range of MIT schools, such as the schools of Engineering, Science, Architecture and Planning, and the MIT Sloan School of Management, as well as other units of the college.

“I look forward to working with Nicolas and the college leadership on raising the profile of CCSE on campus and globally. We will be pursuing a set of initiatives that span from enhancing the visibility of our research and strengthening our CSE PhD program, to expanding professional education offerings and deepening engagement with our alumni and with industry,” says Demanet.

Demanet’s research lies at the intersection of applied mathematics and scientific computing to visualize the structures beneath Earth’s surface. He also has a strong interest in scientific computing, machine learning, inverse problems, and wave propagation. Through his position as principal investigator of the Imaging and Computing Group, Demanet and his students aim to answer fundamental questions in computational seismic imaging to increase the quality and accuracy of mapping and the projection of changes in Earth’s geological structures. The implications of his work are rooted in environmental monitoring, water resources and geothermal energy, and the understanding of seismic hazards, among others.

He joined the MIT faculty in 2009. He received an Alfred P. Sloan Research Fellowship and the U.S. Air Force Young Investigator Award in 2011, and a CAREER award from the National Science Foundation in 2012. He also held the Class of 1954 Career Development Professorship from 2013 to 2016. Prior to coming to MIT, Demanet held the Szegö Assistant Professorship at Stanford University. He completed his undergraduate studies in mathematical engineering and theoretical physics at Universite de Louvain in Belgium, and earned a PhD in applied and computational mathematics at Caltech, where he was awarded the William P. Carey Prize for best dissertation in the mathematical sciences.

For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.

Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a principal investigator at the MIT Laboratory for Information and Decision Systems — was struck by the disparities in how people live.

“It was very clear to me the extent to which inequity is a rampant issue around the world,” Donti says. “From a young age, I knew that I definitely wanted to address that issue.”

That motivation was further stoked by a high school biology teacher, who focused his class on climate and sustainability.

“We learned that climate change, this huge, important issue, would exacerbate inequity,” Donti says. “That really stuck with me and put a fire in my belly.”

So, when Donti enrolled at Harvey Mudd College, she thought she would direct her energy toward the study of chemistry or materials science to create next-generation solar panels.

Those plans, however, were jilted. Donti “fell in love” with computer science, and then discovered work by researchers in the United Kingdom who were arguing that artificial intelligence and machine learning would be essential to help integrate renewables into power grids.

“It was the first time I’d seen those two interests brought together,” she says. “I got hooked and have been working on that topic ever since.”

Pursuing a PhD at Carnegie Mellon University, Donti was able to design her degree to include computer science and public policy. In her research, she explored the need for fundamental algorithms and tools that could manage, at scale, power grids relying heavily on renewables.

“I wanted to have a hand in developing those algorithms and tool kits by creating new machine learning techniques grounded in computer science,” she says. “But I wanted to make sure that the way I was doing the work was grounded both in the actual energy systems domain and working with people in that domain” to provide what was actually needed.

While Donti was working on her PhD, she co-founded a nonprofit called Climate Change AI. Her objective, she says, was to help the community of people involved in climate and sustainability — “be they computer scientists, academics, practitioners, or policymakers” — to come together and access resources, connection, and education “to help them along that journey.”

“In the climate space,” she says, “you need experts in particular climate change-related sectors, experts in different technical and social science tool kits, problem owners, affected users, policymakers who know the regulations — all of those — to have on-the-ground scalable impact.”

When Donti came to MIT in September 2023, it was not surprising that she was drawn by its initiatives directing the application of computer science toward society’s biggest problems, especially the current threat to the health of the planet.

“We’re really thinking about where technology has a much longer-horizon impact and how technology, society, and policy all have to work together,” Donti says. “Technology is not just one-and-done and monetizable in the context of a year.”

Her work uses deep learning models to incorporate the physics and hard constraints of electric power systems that employ renewables for better forecasting, optimization, and control.

“Machine learning is already really widely used for things like solar power forecasting, which is a prerequisite to managing and balancing power grids,” she says. “My focus is, how do you improve the algorithms for actually balancing power grids in the face of a range of time-varying renewables?”

Among Donti’s breakthroughs is a promising solution for power grid operators to be able to optimize for cost, taking into account the actual physical realities of the grid, rather than relying on approximations. While the solution is not yet deployed, it appears to work 10 times faster, and far more cheaply, than previous technologies, and has attracted the attention of grid operators.

Another technology she is developing works to provide data that can be used in training machine learning systems for power system optimization. In general, much data related to the systems is private, either because it is proprietary or because of security concerns. Donti and her research group are working to create synthetic data and benchmarks that, Donti says, “can help to expose some of the underlying problems” in making power systems more efficient.

“The question is,” Donti says, “can we bring our datasets to a point such that they are just hard enough to drive progress?”

For her efforts, Donti has been awarded the U.S. Department of Energy Computational Science Graduate Fellowship and the NSF Graduate Research Fellowship. She was recognized as part of MIT Technology Review’s 2021 list of “35 Innovators Under 35” and Vox’s 2023 “Future Perfect 50.”

Next spring, Donti will co-teach a class called AI for Climate Action with Sara Beery, EECS assistant professor, whose focus is AI for biodiversity and ecosystems, and Abigail Bodner, an assistant professor in Earth, Atmospheric and Planetary Sciences, holding an MIT Schwarzman College of Computing shared position with EECS.

“We’re all super-excited about it,” Donti says.

Coming to MIT, Donti says, “I knew that there would be an ecosystem of people who really cared, not just about success metrics like publications and citation counts, but about the impact of our work on society.”

• The Straits Times, 6 October 2025, Singapore, pA16

• Tamil Murasu, 5 October 2025

• The Straits Times, 6 October 2025, Singapore, pA16

• The Business Times, 7 October 2025, p17

• 8world Online, 5 October 2025

Tokamaks are machines that are meant to hold and harness the power of the sun. These fusion machines use powerful magnets to contain a plasma hotter than the sun’s core and push the plasma’s atoms to fuse and release energy. If tokamaks can operate safely and efficiently, the machines could one day provide clean and limitless fusion energy.

Today, there are a number of experimental tokamaks in operation around the world, with more underway. Most are small-scale research machines built to investigate how the devices can spin up plasma and harness its energy. One of the challenges that tokamaks face is how to safely and reliably turn off a plasma current that is circulating at speeds of up to 100 kilometers per second, at temperatures of over 100 million degrees Celsius.

Such “rampdowns” are necessary when a plasma becomes unstable. To prevent the plasma from further disrupting and potentially damaging the device’s interior, operators ramp down the plasma current. But occasionally the rampdown itself can destabilize the plasma. In some machines, rampdowns have caused scrapes and scarring to the tokamak’s interior — minor damage that still requires considerable time and resources to repair.

Now, scientists at MIT have developed a method to predict how plasma in a tokamak will behave during a rampdown. The team combined machine-learning tools with a physics-based model of plasma dynamics to simulate a plasma’s behavior and any instabilities that may arise as the plasma is ramped down and turned off. The researchers trained and tested the new model on plasma data from an experimental tokamak in Switzerland. They found the method quickly learned how plasma would evolve as it was tuned down in different ways. What’s more, the method achieved a high level of accuracy using a relatively small amount of data. This training efficiency is promising, given that each experimental run of a tokamak is expensive and quality data is limited as a result.

The new model, which the team highlights this week in an open-access Nature Communications paper, could improve the safety and reliability of future fusion power plants.

“For fusion to be a useful energy source it’s going to have to be reliable,” says lead author Allen Wang, a graduate student in aeronautics and astronautics and a member of the Disruption Group at MIT’s Plasma Science and Fusion Center (PSFC). “To be reliable, we need to get good at managing our plasmas.”

The study’s MIT co-authors include PSFC Principal Research Scientist and Disruptions Group leader Cristina Rea, and members of the Laboratory for Information and Decision Systems (LIDS) Oswin So, Charles Dawson, and Professor Chuchu Fan, along with Mark (Dan) Boyer of Commonwealth Fusion Systems and collaborators from the Swiss Plasma Center in Switzerland.

“A delicate balance”

Tokamaks are experimental fusion devices that were first built in the Soviet Union in the 1950s. The device gets its name from a Russian acronym that translates to a “toroidal chamber with magnetic coils.” Just as its name describes, a tokamak is toroidal, or donut-shaped, and uses powerful magnets to contain and spin up a gas to temperatures and energies high enough that atoms in the resulting plasma can fuse and release energy.

Today, tokamak experiments are relatively low-energy in scale, with few approaching the size and output needed to generate safe, reliable, usable energy. Disruptions in experimental, low-energy tokamaks are generally not an issue. But as fusion machines scale up to grid-scale dimensions, controlling much higher-energy plasmas at all phases will be paramount to maintaining a machine’s safe and efficient operation.

“Uncontrolled plasma terminations, even during rampdown, can generate intense heat fluxes damaging the internal walls,” Wang notes. “Quite often, especially with the high-performance plasmas, rampdowns actually can push the plasma closer to some instability limits. So, it’s a delicate balance. And there’s a lot of focus now on how to manage instabilities so that we can routinely and reliably take these plasmas and safely power them down. And there are relatively few studies done on how to do that well.”

Bringing down the pulse

Wang and his colleagues developed a model to predict how a plasma will behave during tokamak rampdown. While they could have simply applied machine-learning tools such as a neural network to learn signs of instabilities in plasma data, “you would need an ungodly amount of data” for such tools to discern the very subtle and ephemeral changes in extremely high-temperature, high-energy plasmas, Wang says.

Instead, the researchers paired a neural network with an existing model that simulates plasma dynamics according to the fundamental rules of physics. With this combination of machine learning and a physics-based plasma simulation, the team found that only a couple hundred pulses at low performance, and a small handful of pulses at high performance, were sufficient to train and validate the new model.

The data they used for the new study came from the TCV, the Swiss “variable configuration tokamak” operated by the Swiss Plasma Center at EPFL (the Swiss Federal Institute of Technology Lausanne). The TCV is a small experimental fusion experimental device that is used for research purposes, often as test bed for next-generation device solutions. Wang used the data from several hundred TCV plasma pulses that included properties of the plasma such as its temperature and energies during each pulse’s ramp-up, run, and ramp-down. He trained the new model on this data, then tested it and found it was able to accurately predict the plasma’s evolution given the initial conditions of a particular tokamak run.

The researchers also developed an algorithm to translate the model’s predictions into practical “trajectories,” or plasma-managing instructions that a tokamak controller can automatically carry out to for instance adjust the magnets or temperature maintain the plasma’s stability. They implemented the algorithm on several TCV runs and found that it produced trajectories that safely ramped down a plasma pulse, in some cases faster and without disruptions compared to runs without the new method.

“At some point the plasma will always go away, but we call it a disruption when the plasma goes away at high energy. Here, we ramped the energy down to nothing,” Wang notes. “We did it a number of times. And we did things much better across the board. So, we had statistical confidence that we made things better.”

The work was supported in part by Commonwealth Fusion Systems (CFS), an MIT spinout that intends to build the world’s first compact, grid-scale fusion power plant. The company is developing a demo tokamak, SPARC, designed to produce net-energy plasma, meaning that it should generate more energy than it takes to heat up the plasma. Wang and his colleagues are working with CFS on ways that the new prediction model and tools like it can better predict plasma behavior and prevent costly disruptions to enable safe and reliable fusion power.

“We’re trying to tackle the science questions to make fusion routinely useful,” Wang says. “What we’ve done here is the start of what is still a long journey. But I think we’ve made some nice progress.”

Additional support for the research came from the framework of the EUROfusion Consortium, via the Euratom Research and Training Program and funded by the Swiss State Secretariat for Education, Research, and Innovation.

MIT engineers have developed a printable aluminum alloy that can withstand high temperatures and is five times stronger than traditionally manufactured aluminum.

The new printable metal is made from a mix of aluminum and other elements that the team identified using a combination of simulations and machine learning, which significantly pruned the number of possible combinations of materials to search through. While traditional methods would require simulating over 1 million possible combinations of materials, the team’s new machine learning-based approach needed only to evaluate 40 possible compositions before identifying an ideal mix for a high-strength, printable aluminum alloy.

When they printed the alloy and tested the resulting material, the team confirmed that, as predicted, the aluminum alloy was as strong as the strongest aluminum alloys that are manufactured today using traditional casting methods.

The researchers envision that the new printable aluminum could be made into stronger, more lightweight and temperature-resistant products, such as fan blades in jet engines. Fan blades are traditionally cast from titanium — a material that is more than 50 percent heavier and up to 10 times costlier than aluminum — or made from advanced composites.

“If we can use lighter, high-strength material, this would save a considerable amount of energy for the transportation industry,” says Mohadeseh Taheri-Mousavi, who led the work as a postdoc at MIT and is now an assistant professor at Carnegie Mellon University.

“Because 3D printing can produce complex geometries, save material, and enable unique designs, we see this printable alloy as something that could also be used in advanced vacuum pumps, high-end automobiles, and cooling devices for data centers,” adds John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering at MIT.

Hart and Taheri-Mousavi provide details on the new printable aluminum design in a paper published in the journal Advanced Materials. The paper’s MIT co-authors include Michael Xu, Clay Houser, Shaolou Wei, James LeBeau, and Greg Olson, along with Florian Hengsbach and Mirko Schaper of Paderborn University in Germany, and Zhaoxuan Ge and Benjamin Glaser of Carnegie Mellon University.

Micro-sizing

The new work grew out of an MIT class that Taheri-Mousavi took in 2020, which was taught by Greg Olson, professor of the practice in the Department of Materials Science and Engineering. As part of the class, students learned to use computational simulations to design high-performance alloys. Alloys are materials that are made from a mix of different elements, the combination of which imparts exceptional strength and other unique properties to the material as a whole.

Olson challenged the class to design an aluminum alloy that would be stronger than the strongest printable aluminum alloy designed to date. As with most materials, the strength of aluminum depends in large part on its microstructure: The smaller and more densely packed its microscopic constituents, or “precipitates,” the stronger the alloy would be.

With this in mind, the class used computer simulations to methodically combine aluminum with various types and concentrations of elements, to simulate and predict the resulting alloy’s strength. However, the exercise failed to produce a stronger result. At the end of the class, Taheri-Mousavi wondered: Could machine learning do better?

“At some point, there are a lot of things that contribute nonlinearly to a material’s properties, and you are lost,” Taheri-Mousavi says. “With machine-learning tools, they can point you to where you need to focus, and tell you for example, these two elements are controlling this feature. It lets you explore the design space more efficiently.”

Layer by layer

In the new study, Taheri-Mousavi continued where Olson’s class left off, this time looking to identify a stronger recipe for aluminum alloy. This time, she used machine-learning techniques designed to efficiently comb through data such as the properties of elements, to identify key connections and correlations that should lead to a more desirable outcome or product.

She found that, using just 40 compositions mixing aluminum with different elements, their machine-learning approach quickly homed in on a recipe for an aluminum alloy with higher volume fraction of small precipitates, and therefore higher strength, than what the previous studies identified. The alloy’s strength was even higher than what they could identify after simulating over 1 million possibilities without using machine learning.

To physically produce this new strong, small-precipitate alloy, the team realized 3D printing would be the way to go instead of traditional metal casting, in which molten liquid aluminum is poured into a mold and is left to cool and harden. The longer this cooling time is, the more likely the individual precipitate is to grow.

The researchers showed that 3D printing, broadly also known as additive manufacturing, can be a faster way to cool and solidify the aluminum alloy. Specifically, they considered laser bed powder fusion (LBPF) — a technique by which a powder is deposited, layer by layer, on a surface in a desired pattern and then quickly melted by a laser that traces over the pattern. The melted pattern is thin enough that it solidfies quickly before another layer is deposited and similarly “printed.” The team found that LBPF’s inherently rapid cooling and solidification enabled the small-precipitate, high-strength aluminum alloy that their machine learning method predicted.

“Sometimes we have to think about how to get a material to be compatible with 3D printing,” says study co-author John Hart. “Here, 3D printing opens a new door because of the unique characteristics of the process — particularly, the fast cooling rate. Very rapid freezing of the alloy after it’s melted by the laser creates this special set of properties.”

Putting their idea into practice, the researchers ordered a formulation of printable powder, based on their new aluminum alloy recipe. They sent the powder — a mix of aluminum and five other elements — to collaborators in Germany, who printed small samples of the alloy using their in-house LPBF system. The samples were then sent to MIT where the team ran multiple tests to measure the alloy’s strength and image the samples’ microstructure.

Their results confirmed the predictions made by their initial machine learning search: The printed alloy was five times stronger than a casted counterpart and 50 percent stronger than alloys designed using conventional simulations without machine learning. The new alloy’s microstructure also consisted of a higher volume fraction of small precipitates, and was stable at high temperatures of up to 400 degrees Celsius — a very high temperature for aluminum alloys.

The researchers are applying similar machine-learning techniques to further optimize other properties of the alloy.

“Our methodology opens new doors for anyone who wants to do 3D printing alloy design,” Taheri-Mousavi says. “My dream is that one day, passengers looking out their airplane window will see fan blades of engines made from our aluminum alloys.”

This work was carried out, in part, using MIT.nano’s characterization facilities.

In a world full of competing sounds, we often have to filter out a lot of noise to hear what’s most important. This critical skill may come more easily for people with musical training, according to scientists at MIT’s McGovern Institute for Brain Research, who used brain imaging to follow what happens when people try to focus their attention on certain sounds.

When Cassia Low Manting, a recent MIT postdoc working in the labs of MIT Professor and McGovern Institute PI John Gabrieli and former McGovern Institute PI Dimitrios Pantazis, asked people to focus on a particular melody while another melody played at the same time, individuals with musical backgrounds were, unsurprisingly, better able to follow the target tune. An analysis of study participants’ brain activity suggests this advantage arises because musical training sharpens neural mechanisms that amplify the sounds they want to listen to while turning down distractions. 

“People can hear, understand, and prioritize multiple sounds around them that flow on a moment-to-moment basis,” explains Gabrieli, who is the Grover Hermann Professor of Health Sciences and Technology at MIT. “This study reveals the specific brain mechanisms that successfully process simultaneous sounds on a moment-to-moment basis and promote attention to the most important sounds. It also shows how musical training alters that processing in the mind and brain, offering insight into how experience shapes the way we listen and pay attention.”

The research team, which also included senior author Daniel Lundqvist at the Karolinska Institute in Sweden, reported their open-access findings Sept. 17 in the journal Science Advances. Manting, who is now at the Karolinska Institute, notes that the research is part of an ongoing collaboration between the two institutions.

Overcoming challenges

Participants in the study had vastly difference backgrounds when it came to music. Some were professional musicians with deep training and experience, while others struggled to differentiate between the two tunes they were played, despite each one’s distinct pitch. This disparity allowed the researchers to explore how the brain’s capacity for attention might change with experience. “Musicians are very fun to study because their brains have been morphed in ways based on their training,” Manting says. “It’s a nice model to study these training effects.”

Still, the researchers had significant challenges to overcome. It has been hard to study how the brain manages auditory attention, because when researchers use neuroimaging to monitor brain activity, they see the brain’s response to all sounds: those that the listener cares most about, as well as those the listener is trying to ignore. It is usually difficult to figure out which brain signals were triggered by which sounds.

Manting and her colleagues overcame this challenge with a method called frequency tagging. Rather than playing the melodies in their experiments at a constant volume, the volume of each melody oscillated, rising and falling with a particular frequency. Each melody had its own frequency, creating detectable patterns in the brain signals that responded to it. “When you play these two sounds simultaneously to the subject and you record the brain signal, you can say, this 39-Hertz activity corresponds to the lower-pitch sound and the 43-Hertz activity corresponds specifically to the higher-pitch sound,” Manting explains. “It is very clean and very clear.”

When they paired frequency tagging with magnetoencephalography, a noninvasive method of monitoring brain activity, the team was able to track how their study participants’ brains responded to each of two melodies during their experiments. While the two tunes played, subjects were instructed to follow either the higher-pitched or the lower-pitched melody. When the music stopped, they were asked about the final notes of the target tune: did they rise or did they fall? The researchers could make this task harder by making the two tunes closer together in pitch, as well as by altering the timing of the notes.

Manting used a survey that asked about musical experience to score each participant’s musicality, and this measure had an obvious effect on task performance: The more musical a person was, the more successful they were at following the tune they had been asked to track.

To look for differences in brain activity that might explain this, the research team developed a new machine-learning approach to analyze their data. They used it to tease apart what was happening in the brain as participants focused on the target tune — even, in some cases, when the notes of the distracting tune played at the exact same time.

Top-down versus bottom-up attention

What they found was a clear separation of brain activity associated with two kinds of attention, known as top-down and bottom-up attention. Manting explains that top-down attention is goal-oriented, involving a conscious focus — the kind of attention listeners called on as they followed the target tune. Bottom-up attention, on the other hand, is triggered by the nature of the sound itself. A fire alarm would be expected to trigger this kind of attention, both with its volume and its suddenness. The distracting tune in the team’s experiments triggered activity associated with bottom-up attention — but more so in some people than in others.

“The more musical someone is, the better they are at focusing their top-down selective attention, and the less the effect of bottom-up attention is,” Manting explains.

Manting expects that musicians use their heightened capacity for top-down attention in other situations, as well. For example, they might be better than others at following a conversation in a room filled with background chatter. “I would put my bet on it that there is a high chance that they will be great at zooming into sounds,” she says.

She wonders, however, if one kind of distraction might actually be harder for a musician to filter out: the sound of their own instrument. Manting herself plays both the piano and the Chinese harp, and she says hearing those instruments is “like someone calling my name.” It’s one of many questions about how musical training affects cognition that she plans to explore in her future work.

In a world full of competing sounds, we often have to filter out a lot of noise to hear what’s most important. This critical skill may come more easily for people with musical training, according to scientists at MIT’s McGovern Institute for Brain Research, who used brain imaging to follow what happens when people try to focus their attention on certain sounds.

When Cassia Low Manting, a recent MIT postdoc working in the labs of MIT Professor and McGovern Institute PI John Gabrieli and former McGovern Institute PI Dimitrios Pantazis, asked people to focus on a particular melody while another melody played at the same time, individuals with musical backgrounds were, unsurprisingly, better able to follow the target tune. An analysis of study participants’ brain activity suggests this advantage arises because musical training sharpens neural mechanisms that amplify the sounds they want to listen to while turning down distractions. 

“People can hear, understand, and prioritize multiple sounds around them that flow on a moment-to-moment basis,” explains Gabrieli, who is the Grover Hermann Professor of Health Sciences and Technology at MIT. “This study reveals the specific brain mechanisms that successfully process simultaneous sounds on a moment-to-moment basis and promote attention to the most important sounds. It also shows how musical training alters that processing in the mind and brain, offering insight into how experience shapes the way we listen and pay attention.”

The research team, which also included senior author Daniel Lundqvist at the Karolinska Institute in Sweden, reported their open-access findings Sept. 17 in the journal Science Advances. Manting, who is now at the Karolinska Institute, notes that the research is part of an ongoing collaboration between the two institutions.

Overcoming challenges

Participants in the study had vastly difference backgrounds when it came to music. Some were professional musicians with deep training and experience, while others struggled to differentiate between the two tunes they were played, despite each one’s distinct pitch. This disparity allowed the researchers to explore how the brain’s capacity for attention might change with experience. “Musicians are very fun to study because their brains have been morphed in ways based on their training,” Manting says. “It’s a nice model to study these training effects.”

Still, the researchers had significant challenges to overcome. It has been hard to study how the brain manages auditory attention, because when researchers use neuroimaging to monitor brain activity, they see the brain’s response to all sounds: those that the listener cares most about, as well as those the listener is trying to ignore. It is usually difficult to figure out which brain signals were triggered by which sounds.

Manting and her colleagues overcame this challenge with a method called frequency tagging. Rather than playing the melodies in their experiments at a constant volume, the volume of each melody oscillated, rising and falling with a particular frequency. Each melody had its own frequency, creating detectable patterns in the brain signals that responded to it. “When you play these two sounds simultaneously to the subject and you record the brain signal, you can say, this 39-Hertz activity corresponds to the lower-pitch sound and the 43-Hertz activity corresponds specifically to the higher-pitch sound,” Manting explains. “It is very clean and very clear.”

When they paired frequency tagging with magnetoencephalography, a noninvasive method of monitoring brain activity, the team was able to track how their study participants’ brains responded to each of two melodies during their experiments. While the two tunes played, subjects were instructed to follow either the higher-pitched or the lower-pitched melody. When the music stopped, they were asked about the final notes of the target tune: did they rise or did they fall? The researchers could make this task harder by making the two tunes closer together in pitch, as well as by altering the timing of the notes.

Manting used a survey that asked about musical experience to score each participant’s musicality, and this measure had an obvious effect on task performance: The more musical a person was, the more successful they were at following the tune they had been asked to track.

To look for differences in brain activity that might explain this, the research team developed a new machine-learning approach to analyze their data. They used it to tease apart what was happening in the brain as participants focused on the target tune — even, in some cases, when the notes of the distracting tune played at the exact same time.

Top-down versus bottom-up attention

What they found was a clear separation of brain activity associated with two kinds of attention, known as top-down and bottom-up attention. Manting explains that top-down attention is goal-oriented, involving a conscious focus — the kind of attention listeners called on as they followed the target tune. Bottom-up attention, on the other hand, is triggered by the nature of the sound itself. A fire alarm would be expected to trigger this kind of attention, both with its volume and its suddenness. The distracting tune in the team’s experiments triggered activity associated with bottom-up attention — but more so in some people than in others.

“The more musical someone is, the better they are at focusing their top-down selective attention, and the less the effect of bottom-up attention is,” Manting explains.

Manting expects that musicians use their heightened capacity for top-down attention in other situations, as well. For example, they might be better than others at following a conversation in a room filled with background chatter. “I would put my bet on it that there is a high chance that they will be great at zooming into sounds,” she says.

She wonders, however, if one kind of distraction might actually be harder for a musician to filter out: the sound of their own instrument. Manting herself plays both the piano and the Chinese harp, and she says hearing those instruments is “like someone calling my name.” It’s one of many questions about how musical training affects cognition that she plans to explore in her future work.

Matthew D. Shoulders, the Class of 1942 Professor of Chemistry, a MacVicar Faculty Fellow, and an associate member of the Broad Institute of MIT and Harvard, has been named head of the MIT Department of Chemistry, effective Jan. 16, 2026. 

“Matt has made pioneering contributions to the chemistry research community through his research on mechanisms of proteostasis and his development of next-generation techniques to address challenges in biomedicine and agriculture,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “He is also a dedicated educator, beloved by undergraduates and graduates alike. I know the department will be in good hands as we double down on our commitment to world-leading research and education in the face of financial headwinds.”

Shoulders succeeds Troy Van Voorhis, the Robert T. Haslam and Bradley Dewey Professor of Chemistry, who has been at the helm since October 2019.

“I am tremendously grateful to Troy for his leadership the past six years, building a fantastic community here in our department. We face challenges, but also many exciting opportunities, as a department in the years to come,” says Shoulders. “One thing is certain: Chemistry innovations are critical to solving pressing global challenges. Through the research that we do and the scientists we train, our department has a huge role to play in shaping the future.”

Shoulders studies how cells fold proteins, and he develops ​and applies novel protein engineering techniques to challenges in biotechnology. His work across chemistry and biochemistry fields including proteostasis, extracellular matrix biology, virology, evolution, and synthetic biology is yielding not just important insights into topics like how cells build healthy tissues and how proteins evolve, but also influencing approaches to disease therapy and biotechnology development.

“Matt is an outstanding researcher whose work touches on fundamental questions about how the cell machinery directs the synthesis and folding of proteins. His discoveries about how that machinery breaks down as a result of mutations or in response to stress has a fundamental impact on how we think about and treat human diseases,” says Van Voorhis.

In one part of Matt's current research program, he is studying how protein folding systems in cells — known as chaperones — shape the evolution of their clients. Amongst other discoveries, his lab has shown that viral pathogens hijack human chaperones to enable their rapid evolution and escape from host immunity. In related recent work, they have discovered that these same chaperones can promote access to malignancy-driving mutations in tumors. Beyond fundamental insights into evolutionary biology, these findings hold potential to open new therapeutic strategies to target cancer and viral infections.

“Matt’s ability to see both the details and the big picture makes him an outstanding researcher and a natural leader for the department,” says Timothy Swager, the John D. MacArthur Professor of Chemistry. “MIT Chemistry can only benefit from his dedication to understanding and addressing the parts and the whole.” 

Shoulders also leads a food security project through the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Shoulders, along with MIT Research Scientist Robbie Wilson, assembled an interdisciplinary team based at MIT to enhance climate resilience in agriculture by improving one of the most inefficient aspects of photosynthesis, the carbon dioxide-fixing plant enzyme RuBisCO. J-WAFS funded this high-risk, high-reward MIT Grand Challenge project in 2023, and it has received further support from federal research agencies and the Grantham Foundation for the Protection of the Environment. 

“Our collaborative team of biochemists and synthetic biologists, computational biologists, and chemists is deeply integrated with plant biologists, creating a robust feedback loop for enzyme engineering,” Shoulders says. “Together, this team is making a concerted effort using state-of-the-art techniques to engineer crop RuBisCO with an eye to helping make meaningful gains in securing a stable crop supply, hopefully with accompanying improvements in both food and water security.”

In addition to his research contributions, Shoulders has taught multiple classes for Course V, including 5.54 (Advances in Chemical Biology) and 5.111 (Principles of Chemical Science), along with a number of other key chemistry classes. His contributions to a 5.111 “bootcamp” through the MITx platform served to address gaps in the classroom curriculum by providing online tools to help undergraduate students better grasp the material in the chemistry General Institute Requirement (GIR). His development of Guided Learning Demonstrations to support first-year chemistry courses at MIT has helped bring the lab to the GIR, and also contributed to the popularity of 5.111 courses offered regularly via MITx.

“I have had the pleasure of teaching with Matt on several occasions, and he is a fantastic educator. He is an innovator both inside and outside the classroom and has an unwavering commitment to his students’ success,” says Van Voorhis of Shoulders, who was named a 2022 MacVicar Faculty Fellow, and who received a Committed to Caring award through the Office of Graduate Education.

Shoulders also founded the MIT Homeschool Internship Program for Science and Technology, which brings high school students to campus for paid summer research experiences in labs across the Institute.

He is a founding member of the Department of Chemistry’s Quality of Life Committee and chair for the last six years, helping to improve all aspects of opportunity, professional development, and experience in the department: “countless changes that have helped make MIT a better place for all,” as Van Voorhis notes, including creating a peer mentoring program for graduate students and establishing universal graduate student exit interviews to collect data for department-wide assessment and improvement.

At the Institute level, Shoulders has served on the Committee on Graduate Programs, Committee on Sexual Misconduct Prevention and Response (in which he co-chaired the provost's working group on the Faculty and Staff Sexual Misconduct Survey), and the Committee on Assessment of Biohazards and Embryonic Stem Cell Research Oversight, among other roles.

Shoulders graduated summa cum laude from Virginia Tech in 2004, earning a BS in chemistry with a minor in biochemistry. He earned a PhD in chemistry at the University of Wisconsin at Madison in 2009 under Professor Ronald Raines. Following an American Cancer Society Postdoctoral Fellowship at Scripps Research Institute, working with professors Jeffery Kelly and Luke Wiseman, Shoulders joined the MIT Department of Chemistry faculty as an assistant professor in 2012. Shoulders also serves as an associate member of the Broad Institute and an investigator at the Center for Musculoskeletal Research at Massachusetts General Hospital.

Among his many awards, Shoulders has received a NIH Director's New Innovator Award under the NIH High-Risk, High-Reward Research Program; an NSF CAREER Award; an American Cancer Society Research Scholar Award; the Camille Dreyfus Teacher-Scholar Award; and most recently the Ono Pharma Foundation Breakthrough Science Award.

MIT chemists have designed a new type of fluorescent molecule that they hope could be used for applications such as generating clearer images of tumors.

The new dye is based on a borenium ion — a positively charged form of boron that can emit light in the red to near-infrared range. Until recently, these ions have been too unstable to be used for imaging or other biomedical applications.

In a study appearing today in Nature Chemistry, the researchers showed that they could stabilize borenium ions by attaching them to a ligand. This approach allowed them to create borenium-containing films, powders, and crystals, all of which emit and absorb light in the red and near-infrared range.

That is important because near-IR light is easier to see when imaging structures deep within tissues, which could allow for clearer images of tumors and other structures in the body.

“One of the reasons why we focus on red to near-IR is because those types of dyes penetrate the body and tissue much better than light in the UV and visible range. Stability and brightness of those red dyes are the challenges that we tried to overcome in this study,” says Robert Gilliard, the Novartis Professor of Chemistry at MIT and the senior author of the study.

MIT research scientist Chun-Lin Deng is the lead author of the paper. Other authors include Bi Youan (Eric) Tra PhD ’25, former visiting graduate student Xibao Zhang, and graduate student Chonghe Zhang.

Stabilized borenium

Most fluorescent imaging relies on dyes that emit blue or green light. Those imaging agents work well in cells, but they are not as useful in tissue because low levels of blue and green fluorescence produced by the body interfere with the signal. Blue and green light also scatters in tissue, limiting how deeply it can penetrate.

Imaging agents that emit red fluorescence can produce clearer images, but most red dyes are inherently unstable and don’t produce a bright signal, because of their low quantum yields (the ratio of fluorescent photons emitted per photon of light is absorbed). For many red dyes, the quantum yield is only about 1 percent.

Among the molecules that can emit near-infrared light are borenium cations —positively charged ions containing an atom of boron attached to three other atoms.

When these molecules were first discovered in the mid-1980s, they were considered “laboratory curiosities,” Gilliard says. These molecules were so unstable that they had to be handled in a sealed container called a glovebox to protect them from exposure to air, which can lead them to break down.

Later, chemists realized they could make these ions more stable by attaching them to molecules called ligands. Working with these more stable ions, Gillliard’s lab discovered in 2019 that they had some unusual properties: Namely, they could respond to changes in temperature by emitting different colors of light.

However, at that point, “there was a substantial problem in that they were still too reactive to be handled in open air,” Gilliard says.

His lab began working on new ways to further stabilize them using ligands known as carbodicarbenes (CDCs), which they reported in a 2022 study. Due to this stabilization, the compounds can now be studied and handled without using a glovebox. They are also resistant to being broken down by light, unlike many previous borenium-based compounds.

In the new study, Gilliard began experimenting with the anions (negatively charged ions) that are a part of the CDC-borenium compounds. Interactions between these anions and the borenium cation generate a phenomenon known as exciton coupling, the researchers discovered. This coupling, they found, shifted the molecules’ emission and absorption properties toward the infrared end of the color spectrum. These molecules also generated a high quantum yield, allowing them to shine more brightly.

“Not only are we in the correct region, but the efficiency of the molecules is also very suitable,” Gilliard says. “We’re up to percentages in the thirties for the quantum yields in the red region, which is considered to be high for that region of the electromagnetic spectrum.”

Potential applications

The researchers also showed that they could convert their borenium-containing compounds into several different states, including solid crystals, films, powders, and colloidal suspensions.

For biomedical imaging, Gilliard envisions that these borenium-containing materials could be encapsulated in polymers, allowing them to be injected into the body to use as an imaging dye. As a first step, his lab plans to work with researchers in the chemistry department at MIT and at the Broad Institute of MIT and Harvard to explore the potential of imaging these materials within cells.

Because of their temperature responsiveness, these materials could also be deployed as temperature sensors, for example, to monitor whether drugs or vaccines have been exposed to temperatures that are too high or low during shipping.

“For any type of application where temperature tracking is important, these types of ‘molecular thermometers’ can be very useful,” Gilliard says.

If incorporated into thin films, these molecules could also be useful as organic light-emitting diodes (OLEDs), particularly in new types of materials such as flexible screens, Gilliard says.

“The very high quantum yields achieved in the near-IR, combined with the excellent environmental stability, make this class of compounds extremely interesting for biological applications,” says Frieder Jaekle, a professor of chemistry at Rutgers University, who was not involved in the study. “Besides the obvious utility in bioimaging, the strong and tunable near-IR emission also makes these new fluorophores very appealing as smart materials for anticounterfeiting, sensors, switches, and advanced optoelectronic devices.”

In addition to exploring possible applications for these dyes, the researchers are now working on extending their color emission further into the near-infrared region, which they hope to achieve by incorporating additional boron atoms. Those extra boron atoms could make the molecules less stable, so the researchers are also working on new types of carbodicarbenes to help stabilize them.

The research was funded by the Arnold and Mabel Beckman Foundation and the National Institutes of Health.

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don’t always want to bring a sledgehammer to a knife fight.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn’s disease flare-ups while leaving the rest of the microbiome largely intact. Using a generative AI model, the team mapped how the compound works, a process that usually takes years but was accelerated here to just months.

“This discovery speaks to a central challenge in antibiotic development,” says Jon Stokes, senior author of a new paper on the work, assistant professor of biochemistry and biomedical sciences at McMaster, and research affiliate at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health. “The problem isn’t finding molecules that kill bacteria in a dish — we’ve been able to do that for a long time. A major hurdle is figuring out what those molecules actually do inside bacteria. Without that detailed understanding, you can’t develop these early-stage antibiotics into safe and effective therapies for patients.”

Enterololin is a stride toward precision antibiotics: treatments designed to knock out only the bacteria causing trouble. In mouse models of Crohn’s-like inflammation, the drug zeroed in on Escherichia coli, a gut-dwelling bacterium that can worsen flares, while leaving most other microbial residents untouched. Mice given enterololin recovered faster and maintained a healthier microbiome than those treated with vancomycin, a common antibiotic.

Pinning down a drug’s mechanism of action, the molecular target it binds inside bacterial cells, normally requires years of painstaking experiments. Stokes’ lab discovered enterololin using a high-throughput screening approach, but determining its target would have been the bottleneck. Here, the team turned to DiffDock, a generative AI model developed at CSAIL by MIT PhD student Gabriele Corso and MIT Professor Regina Barzilay.

DiffDock was designed to predict how small molecules fit into the binding pockets of proteins, a notoriously difficult problem in structural biology. Traditional docking algorithms search through possible orientations using scoring rules, often producing noisy results. DiffDock instead frames docking as a probabilistic reasoning problem: a diffusion model iteratively refines guesses until it converges on the most likely binding mode.

“In just a couple of minutes, the model predicted that enterololin binds to a protein complex called LolCDE, which is essential for transporting lipoproteins in certain bacteria,” says Barzilay, who also co-leads the Jameel Clinic. “That was a very concrete lead — one that could guide experiments, rather than replace them.”

Stokes’ group then put that prediction to the test. Using DiffDock predictions as an experimental GPS, they first evolved enterololin-resistant mutants of E. coli in the lab, which revealed that changes in the mutant’s DNA mapped to lolCDE, precisely where DiffDock had predicted enterololin to bind. They also performed RNA sequencing to see which bacterial genes switched on or off when exposed to the drug, as well as used CRISPR to selectively knock down expression of the expected target. These laboratory experiments all revealed disruptions in pathways tied to lipoprotein transport, exactly what DiffDock had predicted.

“When you see the computational model and the wet-lab data pointing to the same mechanism, that’s when you start to believe you’ve figured something out,” says Stokes.

For Barzilay, the project highlights a shift in how AI is used in the life sciences. “A lot of AI use in drug discovery has been about searching chemical space, identifying new molecules that might be active,” she says. “What we’re showing here is that AI can also provide mechanistic explanations, which are critical for moving a molecule through the development pipeline.”

That distinction matters because mechanism-of-action studies are often a major rate-limiting step in drug development. Traditional approaches can take 18 months to two years, or more, and cost millions of dollars. In this case, the MIT–McMaster team cut the timeline to about six months, at a fraction of the cost.

Enterololin is still in the early stages of development, but translation is already underway. Stokes’ spinout company, Stoked Bio, has licensed the compound and is optimizing its properties for potential human use. Early work is also exploring derivatives of the molecule against other resistant pathogens, such as Klebsiella pneumoniae. If all goes well, clinical trials could begin within the next few years.

The researchers also see broader implications. Narrow-spectrum antibiotics have long been sought as a way to treat infections without collateral damage to the microbiome, but they have been difficult to discover and validate. AI tools like DiffDock could make that process more practical, rapidly enabling a new generation of targeted antimicrobials.

For patients with Crohn’s and other inflammatory bowel conditions, the prospect of a drug that reduces symptoms without destabilizing the microbiome could mean a meaningful improvement in quality of life. And in the bigger picture, precision antibiotics may help tackle the growing threat of antimicrobial resistance.

“What excites me is not just this compound, but the idea that we can start thinking about the mechanism of action elucidation as something we can do more quickly, with the right combination of AI, human intuition, and laboratory experiments,” says Stokes. “That has the potential to change how we approach drug discovery for many diseases, not just Crohn’s.”

“One of the greatest challenges to our health is the increase of antimicrobial-resistant bacteria that evade even our best antibiotics,” adds Yves Brun, professor at the University of Montreal and distinguished professor emeritus at Indiana University Bloomington, who wasn’t involved in the paper. “AI is becoming an important tool in our fight against these bacteria. This study uses a powerful and elegant combination of AI methods to determine the mechanism of action of a new antibiotic candidate, an important step in its potential development as a therapeutic.”

Corso, Barzilay, and Stokes wrote the paper with McMaster researchers Denise B. Catacutan, Vian Tran, Jeremie Alexander, Yeganeh Yousefi, Megan Tu, Stewart McLellan, and Dominique Tertigas, and professors ​​Jakob Magolan, Michael Surette, Eric Brown, and Brian Coombes. Their research was supported, in part, by the Weston Family Foundation; the David Braley Centre for Antibiotic Discovery; the Canadian Institutes of Health Research; the Natural Sciences and Engineering Research Council of Canada; M. and M. Heersink; Canadian Institutes for Health Research; Ontario Graduate Scholarship Award; the Jameel Clinic; and the U.S. Defense Threat Reduction Agency Discovery of Medical Countermeasures Against New and Emerging Threats program.

The researchers posted sequencing data in public repositories and released the DiffDock-L code openly on GitHub.

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don’t always want to bring a sledgehammer to a knife fight.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn’s disease flare-ups while leaving the rest of the microbiome largely intact. Using a generative AI model, the team mapped how the compound works, a process that usually takes years but was accelerated here to just months.

“This discovery speaks to a central challenge in antibiotic development,” says Jon Stokes, senior author of a new paper on the work, assistant professor of biochemistry and biomedical sciences at McMaster, and research affiliate at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health. “The problem isn’t finding molecules that kill bacteria in a dish — we’ve been able to do that for a long time. A major hurdle is figuring out what those molecules actually do inside bacteria. Without that detailed understanding, you can’t develop these early-stage antibiotics into safe and effective therapies for patients.”

Enterololin is a stride toward precision antibiotics: treatments designed to knock out only the bacteria causing trouble. In mouse models of Crohn’s-like inflammation, the drug zeroed in on Escherichia coli, a gut-dwelling bacterium that can worsen flares, while leaving most other microbial residents untouched. Mice given enterololin recovered faster and maintained a healthier microbiome than those treated with vancomycin, a common antibiotic.

Pinning down a drug’s mechanism of action, the molecular target it binds inside bacterial cells, normally requires years of painstaking experiments. Stokes’ lab discovered enterololin using a high-throughput screening approach, but determining its target would have been the bottleneck. Here, the team turned to DiffDock, a generative AI model developed at CSAIL by MIT PhD student Gabriele Corso and MIT Professor Regina Barzilay.

DiffDock was designed to predict how small molecules fit into the binding pockets of proteins, a notoriously difficult problem in structural biology. Traditional docking algorithms search through possible orientations using scoring rules, often producing noisy results. DiffDock instead frames docking as a probabilistic reasoning problem: a diffusion model iteratively refines guesses until it converges on the most likely binding mode.

“In just a couple of minutes, the model predicted that enterololin binds to a protein complex called LolCDE, which is essential for transporting lipoproteins in certain bacteria,” says Barzilay, who also co-leads the Jameel Clinic. “That was a very concrete lead — one that could guide experiments, rather than replace them.”

Stokes’ group then put that prediction to the test. Using DiffDock predictions as an experimental GPS, they first evolved enterololin-resistant mutants of E. coli in the lab, which revealed that changes in the mutant’s DNA mapped to lolCDE, precisely where DiffDock had predicted enterololin to bind. They also performed RNA sequencing to see which bacterial genes switched on or off when exposed to the drug, as well as used CRISPR to selectively knock down expression of the expected target. These laboratory experiments all revealed disruptions in pathways tied to lipoprotein transport, exactly what DiffDock had predicted.

“When you see the computational model and the wet-lab data pointing to the same mechanism, that’s when you start to believe you’ve figured something out,” says Stokes.

For Barzilay, the project highlights a shift in how AI is used in the life sciences. “A lot of AI use in drug discovery has been about searching chemical space, identifying new molecules that might be active,” she says. “What we’re showing here is that AI can also provide mechanistic explanations, which are critical for moving a molecule through the development pipeline.”

That distinction matters because mechanism-of-action studies are often a major rate-limiting step in drug development. Traditional approaches can take 18 months to two years, or more, and cost millions of dollars. In this case, the MIT–McMaster team cut the timeline to about six months, at a fraction of the cost.

Enterololin is still in the early stages of development, but translation is already underway. Stokes’ spinout company, Stoked Bio, has licensed the compound and is optimizing its properties for potential human use. Early work is also exploring derivatives of the molecule against other resistant pathogens, such as Klebsiella pneumoniae. If all goes well, clinical trials could begin within the next few years.

The researchers also see broader implications. Narrow-spectrum antibiotics have long been sought as a way to treat infections without collateral damage to the microbiome, but they have been difficult to discover and validate. AI tools like DiffDock could make that process more practical, rapidly enabling a new generation of targeted antimicrobials.

For patients with Crohn’s and other inflammatory bowel conditions, the prospect of a drug that reduces symptoms without destabilizing the microbiome could mean a meaningful improvement in quality of life. And in the bigger picture, precision antibiotics may help tackle the growing threat of antimicrobial resistance.

“What excites me is not just this compound, but the idea that we can start thinking about the mechanism of action elucidation as something we can do more quickly, with the right combination of AI, human intuition, and laboratory experiments,” says Stokes. “That has the potential to change how we approach drug discovery for many diseases, not just Crohn’s.”

“One of the greatest challenges to our health is the increase of antimicrobial-resistant bacteria that evade even our best antibiotics,” adds Yves Brun, professor at the University of Montreal and distinguished professor emeritus at Indiana University Bloomington, who wasn’t involved in the paper. “AI is becoming an important tool in our fight against these bacteria. This study uses a powerful and elegant combination of AI methods to determine the mechanism of action of a new antibiotic candidate, an important step in its potential development as a therapeutic.”

Corso, Barzilay, and Stokes wrote the paper with McMaster researchers Denise B. Catacutan, Vian Tran, Jeremie Alexander, Yeganeh Yousefi, Megan Tu, Stewart McLellan, and Dominique Tertigas, and professors ​​Jakob Magolan, Michael Surette, Eric Brown, and Brian Coombes. Their research was supported, in part, by the Weston Family Foundation; the David Braley Centre for Antibiotic Discovery; the Canadian Institutes of Health Research; the Natural Sciences and Engineering Research Council of Canada; M. and M. Heersink; Canadian Institutes for Health Research; Ontario Graduate Scholarship Award; the Jameel Clinic; and the U.S. Defense Threat Reduction Agency Discovery of Medical Countermeasures Against New and Emerging Threats program.

The researchers posted sequencing data in public repositories and released the DiffDock-L code openly on GitHub.