Born in Palermo, Sicily, Giorgio Rizzo spent his childhood curious about the natural world. “I have always been fascinated by nature and how plants and animals can adapt and survive in extreme environments,” he says. “Their highly tuned biochemistry, and their incredible ability to create ones of the most complex and beautiful structures in chemistry that we still can’t even achieve in our laboratories.”

As an undergraduate student, he watched as a researcher mounted a towering chromatography column layered with colorful plant chemicals in a laboratory. When the researcher switched on a UV light, the colors turned into fluorescent shades of blue, green, red and pink. “I realized in that exact moment that I wanted to be the same person, separating new unknown compounds from a rare plant with potential pharmaceutical properties,” he recalls.

These experiences set him on a path from a master’s degree in organic chemistry to his current work as a postdoc in the MIT Department of Civil and Environmental Engineering, where he focuses on developing sustainable fertilizers and studying how rare earth elements can boost plant resilience, with the aim of reducing agriculture’s environmental impact.

In the lab of MIT Professor Benedetto Marelli, Rizzo studies plant responses to environmental stressors, such as heat, drought, and prolonged UV irradiation. This includes developing new fertilizers that can be applied as seed coating to help plants grow stronger and enhance their resistance.

“We are working on new formulations of fertilizers that aim to reduce the huge environmental impact of classical practices in agriculture based on NPK inorganic fertilizers,” Rizzo explains. Although they are fundamental to crop yields, their tendency to accumulate in soil is detrimental to the soil health and microbiome living in it. In addition, producing NPK (nitrogen, phosphorus, and potassium) fertilizers is one of the most energy-consuming and polluting chemical processes in the world.

“It is mandatory to reshape our conception of fertilizers and try to rely, at least in part, on alternative products that are safer, cheaper, and more sustainable,” he says.

Recently, Rizzo was awarded a Kavanaugh Fellowship, a program that gives MIT graduate students and postdocs entrepreneurial training and resources to bring their research from the lab to the market. “This prestigious fellowship will help me build a concrete product for a company, adding more value to our research,” he says.

Rizzo hopes their work will help farmers increase their crop yields without compromising soil quality or plant health. A major barrier to adopting new fertilizers is cost, as many farmers rely heavily on each growing season’s output and cannot risk investing in products that may underperform compared to traditional NPK fertilizers. The fertilizers being developed in the Marelli Lab address this challenge by using chitin and chitosan, abundant natural materials that make them far less expensive to produce, which Rizzo hopes will encourage farmers to try them.

“Through the Kavanaugh Fellowship, I will spend this year trying to bring the technology outside the lab to impact the world and meet the need for farmers to support their prosperity,” he says.

Mentorship has been a defining part of his postdoc experience. Rizzo describes Professor Benedetto Marelli as “an incredible mentor” who values his research interests and supports him through every stage of his work. The lab spans a wide range of projects — from plant growth enhancement and precision chemical delivery to wastewater treatment, vaccine development for fish, and advanced biochemical processes. “My colleagues created a stimulant environment with different research topics,” he notes. He is also grateful for the work he does with international institutions, which has helped him build a network of researchers and academics around the world.

Rizzo enjoys the opportunity to mentor students in the lab and appreciates their curiosity and willingness to learn. “It is one of the greatest qualities you can have as a scientist because you must be driven by curiosity to discover the unexpected,” he says.

He describes MIT as a “dynamic and stimulating experience,” but also acknowledges how overwhelming it can be. “You will feel like a small fish in a big ocean,” he says. “But that is exactly what MIT is: an ocean full of opportunities and challenges that are waiting to be solved.”

Beyond his professional work, Rizzo enjoys nature and the arts. An avid reader, he balances his scientific work with literature and history. “I never read about science-related topics — I read about it a lot already for my job,” he says. “I like classic literature, novels, essays, history of nations, and biographies. Often you can find me wandering in museums’ art collections.” Classical art, Renaissance, and Pre-Raphaelites are his favorite artistic currents.

Looking ahead, Rizzo hopes to shift his professional pathway toward startups or companies focused on agrotechnical improvement. His immediate goal is to contribute to initiatives where research has a direct, tangible impact on everyday life.

“I want to pursue the option of being part of a spinout process that would enable my research to have a direct impact in everyday life and help solve agricultural issues,” he adds.

Oct. 16 is World Food Day, a global campaign to celebrate the founding of the Food and Agriculture Organization 80 years ago, and to work toward a healthy, sustainable, food-secure future. More than 670 million people in the world are facing hunger. Millions of others are facing rising obesity rates and struggle to get healthy food for proper nutrition. 

World Food Day calls on not only world governments, but business, academia, the media, and even the youth to take action to promote resilient food systems and combat hunger. This year, the Abdul Latif Jameel Water and Food Systems Laboratory (J-WAFS) is spotlighting an MIT researcher who is working toward this goal by studying food and water systems in the Global South.

J-WAFS seed grants provide funding to early-stage research projects that are unique to prior work. In an 11th round of seed grant funding in 2025, 10 MIT faculty members received support to carry out their cutting-edge water and food research. Ali Aouad PhD ’17, assistant professor of operations management at the MIT Sloan School of Management, was one of those grantees. “I had searched before joining MIT what kind of research centers and initiatives were available that tried to coalesce research on food systems,” Aouad says. “And so, I was very excited about J-WAFS.” 

Aouad gathered more information about J-WAFS at the new faculty orientation session in August 2024, where he spoke to J-WAFS staff and learned about the program’s grant opportunities for water and food research. Later that fall semester, he attended a few J-WAFS seminars on agricultural economics and water resource management. That’s when Aouad knew that his project was perfectly aligned with the J-WAFS mission of securing humankind’s water and food.

Aouad’s seed project focuses on food subsidies. With a background in operations research and an interest in digital platforms, much of his work has centered on aligning supply-side operations with heterogeneous customer preferences. Past projects include ones on retail and matching systems. “I started thinking that these types of demand-driven approaches may be also very relevant to important social challenges, particularly as they relate to food security,” Aouad says. Before starting his PhD at MIT, Aouad worked on projects that looked at subsidies for smallholder farmers in low- and middle-income countries. “I think in the back of my mind, I've always been fascinated by trying to solve these issues,” he noted.

His seed grant project, Optimal subsidy design: Application to food assistance programs, aims to leverage data on preferences and purchasing habits from local grocery stores in India to inform food assistance policy and optimize the design of subsidies. Typical data collection systems, like point-of-sales, are not as readily available in India’s local groceries, making this type of data hard to come by for low-income individuals. “Mom-and-pop stores are extremely important last-mile operators when it comes to nutrition,” he explains. 

For this project, the research team gave local grocers point-of-sale scanners to track purchasing habits. “We aim to develop an algorithm that converts these transactions into some sort of ‘revelation’ of the individuals’ latent preferences,” says Aouad. “As such, we can model and optimize the food assistance programs — how much variety and flexibility is offered, taking into account the expected demand uptake.” He continues, “now, of course, our ability to answer detailed design questions [across various products and prices] depends on the quality of our inference from  the data, and so this is where we need more sophisticated and robust algorithms.”

Following the data collection and model development, the ultimate goal of this research is to inform policy surrounding food assistance programs through an “optimization approach.” Aouad describes the complexities of using optimization to guide policy. “Policies are often informed by domain expertise, legacy systems, or political deliberation. A lot of researchers build rigorous evidence to inform food policy, but it’s fair to say that the kind of approach that I’m proposing in this research is not something that is commonly used. I see an opportunity for bringing a new approach and methodological tradition to a problem that has been central for policy for many decades.” 

The overall health of consumers is the reason food assistance programs exist, yet measuring long-term nutritional impacts and shifts in purchase behavior is difficult. In past research, Aouad notes that the short-term effects of food assistance interventions can be significant. However, these effects are often short-lived. “This is a fascinating question that I don’t think we will be able to address within the space of interventions that we will be considering. However, I think it is something I would like to capture in the research, and maybe develop hypotheses for future work around how we can shift nutrition-related behaviors in the long run.”

While his project develops a new methodology to calibrate food assistance programs, large-scale applications are not promised. “A lot of what drives subsidy mechanisms and food assistance programs is also, quite frankly, how easy it is and how cost-effective it is to implement these policies in the first place,” comments Aouad. Cost and infrastructure barriers are unavoidable to this kind of policy research, as well as sustaining these programs. Aouad’s effort will provide insights into customer preferences and subsidy optimization in a pilot setup, but replicating this approach on a real scale may be costly. Aouad hopes to be able to gather proxy information from customers that would both feed into the model and provide insight into a more cost-effective way to collect data for large-scale implementation.

There is still much work to be done to ensure food security for all, whether it’s advances in agriculture, food-assistance programs, or ways to boost adequate nutrition. As the 2026 seed grant deadline approaches, J-WAFS will continue its mission of supporting MIT faculty as they pursue innovative projects that have practical and real impacts on water and food system challenges.

Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is helping with the former, with tools that comb through catalogs of materials to quickly tag promising candidates.

But once a material is made, verifying its quality still involves scanning it with specialized instruments to validate its performance — an expensive and time-consuming step that can hold up the development and distribution of new technologies.

Now, a new AI tool developed by MIT engineers could help clear the quality-control bottleneck, offering a faster and cheaper option for certain materials-driven industries.

In a study appearing today in the journal Matter, the researchers present “SpectroGen,” a generative AI tool that turbocharges scanning capabilities by serving as a virtual spectrometer. The tool takes in “spectra,” or measurements of a material in one scanning modality, such as infrared, and generates what that material’s spectra would look like if it were scanned in an entirely different modality, such as X-ray. The AI-generated spectral results match, with 99 percent accuracy, the results obtained from physically scanning the material with the new instrument.

Certain spectroscopic modalities reveal specific properties in a material: Infrared reveals a material’s molecular groups, while X-ray diffraction visualizes the material’s crystal structures, and Raman scattering illuminates a material’s molecular vibrations. Each of these properties is essential in gauging a material’s quality and typically requires tedious workflows on multiple expensive and distinct instruments to measure.

With SpectroGen, the researchers envision that a diversity of measurements can be made using a single and cheaper physical scope. For instance, a manufacturing line could carry out quality control of materials by scanning them with a single infrared camera. Those infrared spectra could then be fed into SpectroGen to automatically generate the material’s X-ray spectra, without the factory having to house and operate a separate, often more expensive X-ray-scanning laboratory.

The new AI tool generates spectra in less than one minute, a thousand times faster compared to traditional approaches that can take several hours to days to measure and validate.

“We think that you don’t have to do the physical measurements in all the modalities you need, but perhaps just in a single, simple, and cheap modality,” says study co-author Loza Tadesse, assistant professor of mechanical engineering at MIT. “Then you can use SpectroGen to generate the rest. And this could improve productivity, efficiency, and quality of manufacturing.”

The study’s lead author is former MIT postdoc Yanmin Zhu.

Beyond bonds

Tadesse’s interdisciplinary group at MIT pioneers technologies that advance human and planetary health, developing innovations for applications ranging from rapid disease diagnostics to sustainable agriculture.

“Diagnosing diseases, and material analysis in general, usually involves scanning samples and collecting spectra in different modalities, with different instruments that are bulky and expensive and that you might not all find in one lab,” Tadesse says. “So, we were brainstorming about how to miniaturize all this equipment and how to streamline the experimental pipeline.”

Zhu noted the increasing use of generative AI tools for discovering new materials and drug candidates, and wondered whether AI could also be harnessed to generate spectral data. In other words, could AI act as a virtual spectrometer?

A spectroscope probes a material’s properties by sending light of a certain wavelength into the material. That light causes molecular bonds in the material to vibrate in ways that scatter the light back out to the scope, where the light is recorded as a pattern of waves, or spectra, that can then be read as a signature of the material’s structure.

For AI to generate spectral data, the conventional approach would involve training an algorithm to recognize connections between physical atoms and features in a material, and the spectra they produce. Given the complexity of molecular structures within just one material, Tadesse says such an approach can quickly become intractable.

“Doing this even for just one material is impossible,” she says. “So, we thought, is there another way to interpret spectra?”

The team found an answer with math. They realized that a spectral pattern, which is a sequence of waveforms, can be represented mathematically. For instance, a spectrum that contains a series of bell curves is known as a “Gaussian” distribution, which is associated with a certain mathematical expression, compared to a series of narrower waves, known as a “Lorentzian” distribution, that is described by a separate, distinct algorithm. And as it turns out, for most materials infrared spectra characteristically contain more Lorentzian waveforms, while Raman spectra are more Gaussian, and X-ray spectra is a mix of the two.

Tadesse and Zhu worked this mathematical interpretation of spectral data into an algorithm that they then incorporated into a generative AI model.

“It’s a physics-savvy generative AI that understands what spectra are,” Tadesse says. “And the key novelty is, we interpreted spectra not as how it comes about from chemicals and bonds, but that it is actually math — curves and graphs, which an AI tool can understand and interpret.”

Data co-pilot

The team demonstrated their SpectroGen AI tool on a large, publicly available dataset of over 6,000 mineral samples. Each sample includes information on the mineral’s properties, such as its elemental composition and crystal structure. Many samples in the dataset also include spectral data in different modalities, such as X-ray, Raman, and infrared. Of these samples, the team fed several hundred to SpectroGen, in a process that trained the AI tool, also known as a neural network, to learn correlations between a mineral’s different spectral modalities. This training enabled SpectroGen to take in spectra of a material in one modality, such as in infrared, and generate what a spectra in a totally different modality, such as X-ray, should look like.

Once they trained the AI tool, the researchers fed SpectroGen spectra from a mineral in the dataset that was not included in the training process. They asked the tool to generate a spectra in a different modality, based on this “new” spectra. The AI-generated spectra, they found, was a close match to the mineral’s real spectra, which was originally recorded by a physical instrument. The researchers carried out similar tests with a number of other minerals and found that the AI tool quickly generated spectra, with 99 percent correlation.

“We can feed spectral data into the network and can get another totally different kind of spectral data, with very high accuracy, in less than a minute,” Zhu says.

The team says that SpectroGen can generate spectra for any type of mineral. In a manufacturing setting, for instance, mineral-based materials that are used to make semiconductors and battery technologies could first be quickly scanned by an infrared laser. The spectra from this infrared scanning could be fed into SpectroGen, which would then generate a spectra in X-ray, which operators or a multiagent AI platform can check to assess the material’s quality.

“I think of it as having an agent or co-pilot, supporting researchers, technicians, pipelines and industry,” Tadesse says. “We plan to customize this for different industries’ needs.”

The team is exploring ways to adapt the AI tool for disease diagnostics, and for agricultural monitoring through an upcoming project funded by Google. Tadesse is also advancing the technology to the field through a new startup and envisions making SpectroGen available for a wide range of sectors, from pharmaceuticals to semiconductors to defense.

As costs for diagnostic and sequencing technologies have plummeted in recent years, researchers have collected an unprecedented amount of data around disease and biology. Unfortunately, scientists hoping to go from data to new cures often require help from someone with experience in software engineering.

Now, Watershed Bio is helping scientists and bioinformaticians run experiments and get insights with a platform that lets users analyze complex datasets regardless of their computational skills. The cloud-based platform provides workflow templates and a customizable interface to help users explore and share data of all types, including whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, protein folding, and more.

“Scientists want to learn about the software and data science parts of the field, but they don’t want to become software engineers writing code just to understand their data,” co-founder and CEO Jonathan Wang ’13, SM ’15 says. “With Watershed, they don’t have to.”

Watershed is being used by large and small research teams across industry and academia to drive discovery and decision-making. When new advanced analytic techniques are described in scientific journals, they can be added to Watershed’s platform immediately as templates, making cutting-edge tools more accessible and collaborative for researchers of all backgrounds.

“The data in biology is growing exponentially, and the sequencing technologies generating this data are only getting better and cheaper,” Wang says. “Coming from MIT, this issue was right in my wheelhouse: It’s a tough technical problem. It’s also a meaningful problem because these people are working to treat diseases. They know all this data has value, but they struggle to use it. We want to help them unlock more insights faster.”

No code discovery

Wang expected to major in biology at MIT, but he quickly got excited by the possibilities of building solutions that scaled to millions of people with computer science. He ended up earning both his bachelor’s and master’s degrees from the Department of Electrical Engineering and Computer Science (EECS). Wang also interned at a biology lab at MIT, where he was surprised how slow and labor-intensive experiments were.

“I saw the difference between biology and computer science, where you had these dynamic environments [in computer science] that let you get feedback immediately,” Wang says. “Even as a single person writing code, you have so much at your fingertips to play with.”

While working on machine learning and high-performance computing at MIT, Wang also co-founded a high frequency trading firm with some classmates. His team hired researchers with PhD backgrounds in areas like math and physics to develop new trading strategies, but they quickly saw a bottleneck in their process.

“Things were moving slowly because the researchers were used to building prototypes,” Wang says. “These were small approximations of models they could run locally on their machines. To put those approaches into production, they needed engineers to make them work in a high-throughput way on a computing cluster. But the engineers didn’t understand the nature of the research, so there was a lot of back and forth. It meant ideas you thought could have been implemented in a day took weeks.”

To solve the problem, Wang’s team developed a software layer that made building production-ready models as easy as building prototypes on a laptop. Then, a few years after graduating MIT, Wang noticed technologies like DNA sequencing had become cheap and ubiquitous.

“The bottleneck wasn’t sequencing anymore, so people said, ‘Let’s sequence everything,’” Wang recalls. “The limiting factor became computation. People didn’t know what to do with all the data being generated. Biologists were waiting for data scientists and bioinformaticians to help them, but those people didn’t always understand the biology at a deep enough level.”

The situation looked familiar to Wang.

“It was exactly like what we saw in finance, where researchers were trying to work with engineers, but the engineers never fully understood, and you had all this inefficiency with people waiting on the engineers,” Wang says. “Meanwhile, I learned the biologists are hungry to run these experiments, but there is such a big gap they felt they had to become a software engineer or just focus on the science.”

Wang officially founded Watershed in 2019 with physician Mark Kalinich ’13, a former classmate at MIT who is no longer involved in day-to-day operations of the company.

Wang has since heard from biotech and pharmaceutical executives about the growing complexity of biology research. Unlocking new insights increasingly involves analyzing data from entire genomes, population studies, RNA sequencing, mass spectrometry, and more. Developing personalized treatments or selecting patient populations for a clinical study can also require huge datasets, and there are new ways to analyze data being published in scientific journals all the time.

Today, companies can run large-scale analyses on Watershed without having to set up their own servers or cloud computing accounts. Researchers can use ready-made templates that work with all the most common data types to accelerate their work. Popular AI-based tools like AlphaFold and Geneformer are also available, and Watershed’s platform makes sharing workflows and digging deeper into results easy.

“The platform hits a sweet spot of usability and customizability for people of all backgrounds,” Wang says. “No science is ever truly the same. I avoid the word product because that implies you deploy something and then you just run it at scale forever. Research isn’t like that. Research is about coming up with an idea, testing it, and using the outcome to come up with another idea. The faster you can design, implement, and execute experiments, the faster you can move on to the next one.”

Accelerating biology

Wang believes Watershed is helping biologists keep up with the latest advances in biology and accelerating scientific discovery in the process.

“If you can help scientists unlock insights not a little bit faster, but 10 or 20 times faster, it can really make a difference,” Wang says.

Watershed is being used by researchers in academia and in companies of all sizes. Executives at biotech and pharmaceutical companies also use Watershed to make decisions about new experiments and drug candidates.

“We’ve seen success in all those areas, and the common thread is people understanding research but not being an expert in computer science or software engineering,” Wang says. “It’s exciting to see this industry develop. For me, it’s great being from MIT and now to be back in Kendall Square where Watershed is based. This is where so much of the cutting-edge progress is happening. We’re trying to do our part to enable the future of biology.”

More than 300 million people worldwide are living with rare disorders — many of which have a genetic cause and affect the brain and nervous system — yet the vast majority of these conditions lack an approved therapy. Because each rare disorder affects fewer than 65 out of every 100,000 people, studying these disorders and creating new treatments for them is especially challenging.

Thanks to a generous philanthropic gift from Ana Méndez ’91 and Rajeev Jayavant ’86, EE ’88, SM ’88, MIT is now poised to fill gaps in this research landscape. By establishing the Rare Brain Disorders Nexus — or RareNet — at MIT's McGovern Institute for Brain Research, the alumni aim to convene leaders in neuroscience research, clinical medicine, patient advocacy, and industry to streamline the lab-to-clinic pipeline for rare brain disorder treatments.

“Ana and Rajeev’s commitment to MIT will form crucial partnerships to propel the translation of scientific discoveries into promising therapeutics and expand the Institute’s impact on the rare brain disorders community,” says MIT President Sally Kornbluth. “We are deeply grateful for their pivotal role in advancing such critical science and bringing attention to conditions that have long been overlooked.”

Building new coalitions

Several hurdles have slowed the lab-to-clinic pipeline for rare brain disorder research. It is difficult to secure a sufficient number of patients per study, and current research efforts are fragmented, since each study typically focuses on a single disorder (there are more than 7,000 known rare disorders, according to the World Health Organization). Pharmaceutical companies are often reluctant to invest in emerging treatments due to a limited market size and the high costs associated with preparing drugs for commercialization.

Méndez and Jayavant envision that RareNet will finally break down these barriers. “Our hope is that RareNet will allow leaders in the field to come together under a shared framework and ignite scientific breakthroughs across multiple conditions. A discovery for one rare brain disorder could unlock new insights that are relevant to another,” says Jayavant. “By congregating the best minds in the field, we are confident that MIT will create the right scientific climate to produce drug candidates that may benefit a spectrum of uncommon conditions.”

Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor in Neuroscience and associate director of the McGovern Institute, will serve as RareNet’s inaugural faculty director. Feng holds a strong record of advancing studies on therapies for neurodevelopmental disorders, including autism spectrum disorders, Williams syndrome, and uncommon forms of epilepsy. His team’s gene therapy for Phelan-McDermid syndrome, a rare and profound autism spectrum disorder, has been licensed to Jaguar Gene Therapy and is currently undergoing clinical trials. “RareNet pioneers a unique model for biomedical research — one that is reimagining the role academia can play in developing therapeutics,” says Feng.

RareNet plans to deploy two major initiatives: a global consortium and a therapeutic pipeline accelerator. The consortium will form an international network of researchers, clinicians, and patient groups from the outset. It seeks to connect siloed research efforts, secure more patient samples, promote data sharing, and drive a strong sense of trust and goal alignment across the RareNet community. Partnerships within the consortium will support the aim of the therapeutic pipeline accelerator: to de-risk early lab discoveries and expedite their translation to clinic. By fostering more targeted collaborations — especially between academia and industry — the accelerator will prepare potential treatments for clinical use as efficiently as possible.

MIT labs are focusing on four uncommon conditions in the first wave of RareNet projects: Rett syndrome, prion disease, disorders linked to SYNGAP1 mutations, and Sturge-Weber syndrome. The teams are working to develop novel therapies that can slow, halt, or reverse dysfunctions in the brain and nervous system.

These efforts will build new bridges to connect key stakeholders across the rare brain disorders community and disrupt conventional research approaches. “Rajeev and I are motivated to seed powerful collaborations between MIT researchers, clinicians, patients, and industry,” says Méndez. “Guoping Feng clearly understands our goal to create an environment where foundational studies can thrive and seamlessly move toward clinical impact.”

“Patient and caregiver experiences, and our foreseeable impact on their lives, will guide us and remain at the forefront of our work,” Feng adds. “For far too long has the rare brain disorders community been deprived of life-changing treatments — and, importantly, hope. RareNet gives us the opportunity to transform how we study these conditions, and to do so at a moment when it’s needed more than ever.”

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.

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 HRasG12V 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.

The Massachusetts Institute of Technology Investment Management Company (MITIMCo) announced today that MIT’s unitized pool of endowment and other MIT funds generated an investment return of 14.8 percent during the fiscal year ending June 30, 2025, as measured using valuations received within one month of fiscal year end. At the end of the fiscal year, MIT’s endowment funds totaled $27.4 billion, excluding pledges. Over the 10 years ending June 30, 2025, MIT generated an annualized return of 10.7 percent.

The endowment is the bedrock of MIT’s finances, made possible by gifts from alumni and friends for more than a century. The use of the endowment is governed by a state law that requires MIT to maintain each endowed gift as a permanent fund, preserve its purchasing power, and spend it as directed by its original donor. Most of the endowment’s funds are restricted and must be used for a specific purpose. MIT uses the bulk of the income these endowed gifts generate to support financial aid, research, and education.

The endowment supports 50 percent of undergraduate tuition, helping to enable the Institute’s need-blind undergraduate admissions policy, which ensures that an MIT education is accessible to all qualified candidates regardless of financial resources. MIT works closely with all families of undergraduates who qualify for financial aid to develop an individual affordability plan tailored to their financial circumstances. In 2024-25, the average need-based MIT undergraduate scholarship was $62,127. Fifty-seven percent of MIT undergraduates received need-based financial aid, and 39 percent of MIT undergraduate students received scholarship funding from MIT and other sources sufficient to cover the total cost of tuition.

Effective in fiscal 2026, MIT enhanced undergraduate financial aid, ensuring that all families with incomes below $200,000 and typical assets have tuition fully covered by scholarships, and that families with incomes below $100,000 and typical assets pay nothing at all for their students’ MIT education. Eighty-eight percent of seniors who graduated in academic year 2025 graduated with no debt.

MITIMCo is a unit of MIT, created to manage and oversee the investment of the Institute’s endowment, retirement, and operating funds.

MIT’s Report of the Treasurer for fiscal year 2025, which details the Institute’s annual financial performance, was made available publicly today.

Innovator, futurist, and author Ray Kurzweil ’70 emphasized his optimism about artificial intelligence, and technological progress generally, in a lecture on Wednesday while accepting MIT’s Robert A. Muh Alumni Award from the School of Humanities, Arts, and Social Sciences (SHASS).

Kurzweil offered his signature high-profile forecasts about how AI and computing will entirely blend with human functionality, and proposed that AI will lead to monumental gains in longevity, medicine, and other realms of life.

“People do not appreciate that the rate of progress is accelerating,” Kurzweil said, forecasting “incredible breakthroughs” over the next two decades.

Kurzweil delivered his lecture, titled “Reinventing Intelligence,” in the Thomas Tull Concert Hall of the Edward and Joyce Linde Music Building, which opened earlier in 2025 on the MIT campus.

The Muh Award was founded and endowed by Robert A. Muh ’59 and his wife Berit, and is one of the leading alumni honors granted by SHASS and MIT. Muh, a life member emeritus of the MIT Corporation, established the award, which is granted every two years for “extraordinary contributions” by alumni in the humanities, arts, and social sciences.

Robert and Berit Muh were both present at the lecture, along with their daughter Carrie Muh ’96, ’97, SM ’97.

Agustín Rayo, dean of SHASS, offered introductory remarks, calling Kurzweil “one of the most prolific thinkers of our time.” Rayo added that Kurzweil “has built his life and career on the belief that ideas change the world, and change it for the better.”

Kurzweil has been an innovator in language recognition technologies, developing advances and founding companies that have served people who are blind or low-vision, and helped in music creation. He is also a best-selling author who has heralded advances in computing capabilities, and even the merging of human and machines.

The initial segment of Kurzweil’s lecture was autobiographical in focus, reflecting on his family and early years. The families of both of Kurzweil’s parents fled the Nazis in Europe, seeking refuge in the U.S., with the belief that people could create a brighter future for themselves.

“My parents taught me the power of ideas can really change the world,” Kurzweil said.

Showing an early interest in how things worked, Kurzweil had decided to become an inventor by about the age of 7, he recalled. He also described his mother as being tremendously encouraging to him as a child. The two would take walks together, and the young Kurzweil would talk about all the things he imagined inventing.

“I would tell her my ideas and no matter how fantastical they were, she believed them,” he said. “Now other parents might have simply chuckled … but she actually believed my ideas, and that actually gave me my confidence, and I think confidence is important in succeeding.”

He became interested in computing by the early 1960s and majored in both computer science and literature as an MIT undergraduate.

Kurzweil has a long-running association with MIT extending far beyond his undergraduate studies. He served as a member of the MIT Corporation from 2005 to 2012 and was the 2001 recipient of the $500,000 Lemelson-MIT Prize, an award for innovation, for his development of reading technology.

“MIT has played a major role in my personal and professional life over the years,” Kurzweil said, calling himself “truly honored to receive this award.” Addressing Muh, he added: “Your longstanding commitment to our alma mater is inspiring.”

After graduating from MIT, Kurzweil launched a successful career developing innovative computing products, including one that recognized text across all fonts and could produce an audio reading. He also developed leading-edge music synthesizers, among many other advances.

In a corresponding part of his career, Kurzweil has become an energetic author, whose best-known books include “The Age of Intelligent Machines” (1990), “The Age of Spiritual Machines” (1999), “The Singularity Is Near” (2005), and “The Singularity Is Nearer” (2024), among many others.

Kurzweil was recently named chief AI officer of Beyond Imagination, a robotics firm he co-founded; he has also held a position at Google in recent years, working on natural language technologies.

In his remarks, Kurzweil underscored his view that, as exemplified and enabled by the growth of computing power over time, technological innovation moves at an exponential pace.

“People don’t really think about exponential growth; they think about linear growth,” Kurzweil said.

This concept, he said, makes him confident that a string of innovations will continue at remarkable speed.

“One of the bigger transformations we’re going to see from AI in the near term is health and medicine,” Kurweil said, forecasting that human medical trials will be replaced by simulated “digital trials.”

Kurzweil also believes computing and AI advances can lead to so many medical advances it will soon produce a drastic improvement in human longevity.

“These incredible breakthroughs are going to lead to what we’ll call longevity escape velocity,” Kurzweil said. “By roughly 2032 when you live through a year, you’ll get back an entire year from scientific progress, and beyond that point you’ll get back more than a year for every year you live, so you’ll be going back into time as far as your health is concerned,” Kurweil said. He did offer that these advances will “start” with people who are the most diligent about their health.

Kurzweil also outlined one of his best-known forecasts, that AI and people will be combined. “As we move forward, the lines between humans and technology will blur, until we are … one and the same,” Kurzweil said. “This is how we learn to merge with AI. In the 2030s, robots the size of molecules will go into our brains, noninvasively, through the capillaries, and will connect our brains directly to the cloud. Think of it like having a phone, but in your brain.”

“By 2045, once we have fully merged with AI, our intelligence will no longer be constrained … it will expand a millionfold,” he said. “This is what we call the singularity.”

To be sure, Kurzweil acknowledged, “Technology has always been a double-edged sword,” given that a drone can deliver either medical supplies or weaponry. “Threats of AI are real, must be taken seriously, [and] I think we are doing that,” he said. In any case, he added, we have “a moral imperative to realize the promise of new technologies while controlling the peril.” He concluded: “We are not doomed to fail to control any of these risks.” 

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.”