The blockbuster climate deal made history a decade ago. But its record at taming climate change is spotty.

President Donald Trump is approving disaster requests at a slower pace in his second term than his predecessor, former President Joe Biden.

The decision is a win for Democratic-led states that sued to save the program, which helps states gird for natural disasters.

Some communities are taking their concerns about intensifying climate disasters to the courts.

The U.S. may be able to keep oil worth as much as $100 million after seizing an oil tanker headed to Cuba.

The birthday of the founding treaty of climate negotiations arrives just as the fight against climate change appears to lose momentum.

Governments and carmakers say shifting away from current technology by 2035 is too aggressive and risks killing a core industry.

The heating law provoked an outcry when it was introduced by Germany’s previous government of Social Democrats and Greens.

Figures released by Israel's military suggest it hasn't met the ceasefire stipulation of allowing 600 trucks of aid into Gaza a day.

Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county.

They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships.

Standard machine-learning methods excel at making predictions and sometimes provide uncertainties, like confidence intervals, for these predictions. However, they generally don’t provide estimates or confidence intervals when determining whether two variables are related. Other methods have been developed specifically to address this association problem and provide confidence intervals. But, in spatial settings, MIT researchers found these confidence intervals can be completely off the mark.

When variables like air pollution levels or precipitation change across different locations, common methods for generating confidence intervals may claim a high level of confidence when, in fact, the estimation completely failed to capture the actual value. These faulty confidence intervals can mislead the user into trusting a model that failed.

After identifying this shortfall, the researchers developed a new method designed to generate valid confidence intervals for problems involving data that vary across space. In simulations and experiments with real data, their method was the only technique that consistently generated accurate confidence intervals.

This work could help researchers in fields like environmental science, economics, and epidemiology better understand when to trust the results of certain experiments.

“There are so many problems where people are interested in understanding phenomena over space, like weather or forest management. We’ve shown that, for this broad class of problems, there are more appropriate methods that can get us better performance, a better understanding of what is going on, and results that are more trustworthy,” says Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS), a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society, an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of this study.

Broderick is joined on the paper by co-lead authors David R. Burt, a postdoc, and Renato Berlinghieri, an EECS graduate student; and Stephen Bates an assistant professor in EECS and member of LIDS. The research was recently presented at the Conference on Neural Information Processing Systems.

Invalid assumptions

Spatial association involves studying how a variable and a certain outcome are related over a geographic area. For instance, one might want to study how tree cover in the United States relates to elevation.

To solve this type of problem, a scientist could gather observational data from many locations and use it to estimate the association at a different location where they do not have data.

The MIT researchers realized that, in this case, existing methods often generate confidence intervals that are completely wrong. A model might say it is 95 percent confident its estimation captures the true relationship between tree cover and elevation, when it didn’t capture that relationship at all.

After exploring this problem, the researchers determined that the assumptions these confidence interval methods rely on don’t hold up when data vary spatially.

Assumptions are like rules that must be followed to ensure results of a statistical analysis are valid. Common methods for generating confidence intervals operate under various assumptions.

First, they assume that the source data, which is the observational data one gathered to train the model, is independent and identically distributed. This assumption implies that the chance of including one location in the data has no bearing on whether another is included. But, for example, U.S. Environmental Protection Agency (EPA) air sensors are placed with other air sensor locations in mind.

Second, existing methods often assume that the model is perfectly correct, but this assumption is never true in practice. Finally, they assume the source data are similar to the target data where one wants to estimate.

But in spatial settings, the source data can be fundamentally different from the target data because the target data are in a different location than where the source data were gathered.

For instance, a scientist might use data from EPA pollution monitors to train a machine-learning model that can predict health outcomes in a rural area where there are no monitors. But the EPA pollution monitors are likely placed in urban areas, where there is more traffic and heavy industry, so the air quality data will be much different than the air quality data in the rural area.

In this case, estimates of association using the urban data suffer from bias because the target data are systematically different from the source data.

A smooth solution

The new method for generating confidence intervals explicitly accounts for this potential bias.

Instead of assuming the source and target data are similar, the researchers assume the data vary smoothly over space.

For instance, with fine particulate air pollution, one wouldn’t expect the pollution level on one city block to be starkly different than the pollution level on the next city block. Instead, pollution levels would smoothly taper off as one moves away from a pollution source.

“For these types of problems, this spatial smoothness assumption is more appropriate. It is a better match for what is actually going on in the data,” Broderick says.

When they compared their method to other common techniques, they found it was the only one that could consistently produce reliable confidence intervals for spatial analyses. In addition, their method remains reliable even when the observational data are distorted by random errors.

In the future, the researchers want to apply this analysis to different types of variables and explore other applications where it could provide more reliable results.

This research was funded, in part, by an MIT Social and Ethical Responsibilities of Computing (SERC) seed grant, the Office of Naval Research, Generali, Microsoft, and the National Science Foundation (NSF).

The School of Science welcomed 11 new faculty members in 2024.

Shaoyun Bai researches symplectic topology, the study of even-dimensional spaces whose properties are reflected by two-dimensional surfaces inside them. He is interested in this area’s interaction with other fields, including algebraic geometry, algebraic topology, geometric topology, and dynamics. He has been developing new tool kits for counting problems from moduli spaces, which have been applied to classical questions, including the Arnold conjecture, periodic points of Hamiltonian maps, higher-rank Casson invariants, enumeration of embedded curves, and topology of symplectic fibrations.

Bai completed his undergraduate studies at Tsinghua University in 2017 and earned his PhD in mathematics from Princeton University in 2022, advised by John Pardon. Bai then held visiting positions at MSRI (now known as Simons Laufer Mathematical Sciences Institute) as a McDuff Postdoctoral Fellow and at the Simons Center for Geometry and Physics, and he was a Ritt Assistant Professor at Columbia University. He joined the MIT Department of Mathematics as an assistant professor in 2024.

Abigail Bodner investigates turbulence in the upper ocean using remote sensing measurements, in-situ ocean observations numerical simulations, climate models, and machine learning. Her research explores how the small-scale physics of turbulence near the ocean surface impacts the large-scale climate. 

Bodner earned a BS and MS from Tel Aviv University studying mathematics and geophysics, atmospheric and planetary sciences. She then went on to Brown University, earning an MS in applied mathematics before completing her PhD studies in 2021 in Earth, environmental, and planetary science. Prior to coming to MIT, Bodner was a Simons Society Junior Fellow at New York University. Bodner joined the Department of Earth, Atmospheric and Planetary Sciences (EAPS) faculty in 2024, with a shared appointment in the Department of Electrical Engineering and Computer Science.

Jacopo Borga is interested in probability theory and its connections to combinatorics, and in mathematical physics. He studies various random combinatorial structures — mathematical objects such as graphs or permutations — and their patterns and behavior at a large scale. This research includes random permutons, meanders, multidimensional constrained Brownian motions, Schramm-Loewner evolutions, and Liouville quantum gravity. 

Borga earned bachelor’s and master’s degrees in mathematics from the Università degli Studi di Padova, and a master’s degree in mathematics from Université Sorbonne Paris Cité (USPC), then proceeded to complete a PhD in mathematics at Unstitut für Mathematik at the Universität Zürich. Borga was an assistant professor at Stanford University before joining MIT as an assistant professor of mathematics in 2024.

Linlin Fan aims to decipher the neural codes underlying learning and memory and to identify the physical basis of learning and memory. Her research focus is on the learning rules of brain circuits — what kinds of activity trigger the encoding and storing of information — how these learning rulers are implemented, and how memories can be inferred from mapping neural functional connectivity patterns. To answer these questions, Fan’s group leverages high-precision, all-optical technologies to map and control the electrical charges of neurons within the brain.

Fan earned her PhD at Harvard University after undergraduate studies at Peking University in China. She joined the MIT Department of Brain and Cognitive Sciences as the Samuel A. Goldblith Career Development Professor of Applied Biology, and the Picower Institute for Learning and Memory as an investigator in January 2024. Previously, Fan worked as a postdoc at Stanford University.

Whitney Henry investigates ferroptosis, a type of cell death dependent on iron, to uncover how oxidative stress, metabolism, and immune signaling intersect to shape cell fate decisions. Her research has defined key lipid metabolic and iron homeostatic programs that regulate ferroptosis susceptibility. By uncovering the molecular factors influencing ferroptosis susceptibility, investigating its effects on the tumor microenvironment, and developing innovative methods to manipulate ferroptosis resistance in living organisms, Henry’s lab aims to gain a comprehensive understanding of the therapeutic potential of ferroptosis, especially to target highly metastatic, therapy-resistant cancer cells.

Henry received her bachelor's degree in biology with a minor in chemistry from Grambling State University and her PhD from Harvard University. Following her doctoral studies, she worked at the Whitehead Institute for Biomedical Research and was supported by fellowships from the Jane Coffin Childs Memorial Fund for Medical Research and the Ludwig Center at MIT. Henry joined the MIT faculty in 2024 as an assistant professor in the Department of Biology and a member of the Koch Institute for Integrative Cancer Research, and was recently named the Robert A. Swanson (1969) Career Development Professor of Life Sciences and a HHMI Freeman Hrabowski Scholar.

Gian Michele Innocenti is an experimental physicist who probes new regimes of quantum chromodynamics (QCD) through collisions of ultra relativistic heavy ions at the Large Hadron Collider. He has developed advanced analysis techniques and data-acquisition strategies that enable novel measurements of open heavy-flavor and jet production in hadronic and ultraperipheral heavy-ion collisions, shedding light on the properties of high-temperature QCD matter and parton dynamics in Lorentz-contracted nuclei. He leads the MIT Pixel𝜑 program, which exploits CMOS MAPS technology to build a high-precision tracking detector for the ePIC experiment at the Electron–Ion Collider.

Innocenti received his PhD in particle and nuclear physics at the University of Turin in Italy in early 2014. He then joined the MIT heavy-ion group in the Laboratory of Nuclear Science in 2014 as a postdoc, followed by a staff research physicist position at CERN in 2018. Innocenti joined the MIT Department of Physics as an assistant professor in January 2024.

Mathematician Christoph Kehle's research interests lie at the intersection of analysis, geometry, and partial differential equations. In particular, he focuses on the Einstein field equations of general relativity and our current understanding of gravitation, which describe how matter and energy shape spacetime. His work addresses the Strong Cosmic Censorship conjecture, singularities in black hole interiors, and the dynamics of extremal black holes.

Prior to joining MIT, Kehle was a junior fellow at ETH Zürich and a member at the Institute for Advanced Study in Princeton. He earned his bachelor’s and master’s degrees at Ludwig Maximilian University and Technical University of Munich, and his PhD in 2020 from the University of Cambridge. Kehle joined the Department of Mathematics as an assistant professor in July 2024.

Aleksandr Logunov is a mathematician specializing in harmonic analysis and geometric analysis. He has developed novel techniques for studying the zeros of solutions to partial differential equations and has resolved several long-standing problems, including Yau’s conjecture, Nadirashvili’s conjecture, and Landis’ conjectures.

Logunov earned his PhD in 2015 from St. Petersburg State University. He then spent two years as a postdoc at Tel Aviv University, followed by a year as a member of the Institute for Advanced Study in Princeton. In 2018, he joined Princeton University as an assistant professor. In 2020, he spent a semester at Tel Aviv University as an IAS Outstanding Fellow, and in 2021, he was appointed full professor at the University of Geneva. Logunov joined MIT as a full professor in the Department of Mathematics in January 2024.

Lyle Nelson is a sedimentary geologist studying the co-evolution of life and surface environments across pivotal transitions in Earth history, especially during significant ecological change — such as extinction events and the emergence of new clades — and during major shifts in ocean chemistry and climate. Studying sedimentary rocks that were tectonically uplifted and are now exposed in mountain belts around the world, Nelson’s group aims to answer questions such as how the reorganization of continents influenced the carbon cycle and climate, the causes and effects of ancient ice ages, and what factors drove the evolution of early life forms and the rapid diversification of animals during the Cambrian period.

Nelson earned a bachelor’s degree in earth and planetary sciences from Harvard University in 2015 and then worked as an exploration geologist before completing his PhD at Johns Hopkins University in 2022. Prior to coming to MIT, he was an assistant professor in the Department of Earth Sciences at Carleton University in Ontario, Canada. Nelson joined the EAPS faculty in 2024.

Protein evolution is the process by which proteins change over time through mechanisms such as mutation or natural selection. Biologist Sergey Ovchinnikov uses phylogenetic inference, protein structure prediction/determination, protein design, deep learning, energy-based models, and differentiable programming to tackle evolutionary questions at environmental, organismal, genomic, structural, and molecular scales, with the aim of developing a unified model of protein evolution.

Ovchinnikov received his BS in micro/molecular biology from Portland State University in 2010 and his PhD in molecular and cellular biology from the University of Washington in 2017. He was next a John Harvard Distinguished Science Fellow at Harvard University until 2023. Ovchinnikov joined MIT as an assistant professor of biology in January 2024.

Shu-Heng Shao explores the structural aspects of quantum field theories and lattice systems. Recently, his research has centered on generalized symmetries and anomalies, with a particular focus on a novel type of symmetry without an inverse, referred to as non-invertible symmetries. These new symmetries have been identified in various quantum systems, including the Ising model, Yang-Mills theories, lattice gauge theories, and the Standard Model. They lead to new constraints on renormalization group flows, new conservation laws, and new organizing principles in classifying phases of quantum matter.

Shao obtained his BS in physics from National Taiwan University in 2010, and his PhD in physics from Harvard University in 2016. He was then a five-year long-term member at the Institute for Advanced Study in Princeton before he moved to the Yang Institute for Theoretical Physics at Stony Brook University as an assistant professor in 2021. In 2024, he joined the MIT faculty as an assistant professor of physics.

Glioblastoma is the most common form of brain cancer in adults, and its consequences are usually quick and fatal. After receiving standard-of-care treatment (surgery followed by radiation and chemotherapy), fewer than half of patients will survive longer than 15 months. Only 5 percent of patients survive longer than five years.

Researchers have explored immune checkpoint inhibitors as an avenue for boosting glioblastoma survival rates. This type of immunotherapy, which has proven effective against a range of tumor types, turns off a molecular switch that prevents T cells from attacking cancer cells. The patient’s own immune system is then able to clear the tumor. 

However, glioblastoma is unusually resistant to attack by T cells, rendering immune checkpoint inhibitors ineffective. The culprit is a different immune cell, macrophages, which have been recruited to tumors, where they support tumor growth while suppressing the ability of T cells to infiltrate and attack tumors.

A team of researchers led by Forest White at the MIT Koch Institute for Integrative Cancer Research used sophisticated immune profiling tools to map out how macrophages evolve from a first-line defense against cancer and other pathogens into a shield that protects the glioblastoma tumor — as well as how the tumor cells themselves are transformed by the encounter.

“Looking at the co-evolution of both cell types is key,” says White, who is also the Ned C. (1949) and Janet C. (Bemis) Rice Professor in the Department of Biological Engineering. “It’s a little bit like what happens when a new family moves into a neighborhood: The family members’ lives change, but so do the social dynamics of the people around them. Whether you’re mixing people or cells, you won’t be able to predict how they will interact, even if you know both well.”

“By looking at what happens when macrophages move into the tumor, we can observe changes to both types of cells that we wouldn’t otherwise be able to see,” says Yufei Cui, a PhD candidate in the White Laboratory. “We were able to identify new targets for both glioblastoma and macrophages that could be used to develop therapies that, when delivered in combination with immune checkpoint inhibitors, more effectively treat glioblastoma.”

The study, appearing recently in Cancer Research, includes Stefani Spranger, associate professor of biology and member of the MIT Koch Institute, and Darrell Irvine, former member of the Koch Institute and now professor at the Scripps Research Institute.

As in other cancers, macrophages play a pivotal role in glioblastoma development and resistance to immune therapies. In laboratory models, inhibiting the activity of tumor-associated macrophages has been found to slow glioblastoma growth, but that success has not translated to studies of human patients. While the overall strategy of targeting glioblastoma-associated macrophages is promising, new targets — derived from models that more accurately reproduce the cell interactions in patient tumors — need to be identified.

One approach to discovering such targets is a specialty of the White lab: profiling cells’ immunopeptidomes — the repertoires of antigens presented on the surfaces of cancer cells, macrophages, and many other types of cells. Surface-presenting antigens are a window into the internal state of the cell: The antigens derive from proteins produced as the cell carries out different functions and responds to its environment. By binding to surface antigens, T cells and other immune cells can monitor cells for dysfunction and respond to them. 

The White lab has developed sophisticated methods for immunopeptidome profiling, combining methods such as liquid chromatography and mass spectrometry to isolate cell surface antigens — in this case, from glioblastoma and macrophage cells cultured in isolation and together — and quantifying changes in expression over time. The researchers identified over 800 peptides in macrophages that either increased or decreased in expression when cultured with glioblastoma cells. Peptides with the biggest gains in expression under co-cultivation derived from 33 source proteins, mostly related to cytokine signaling that promotes tumor aggression and suppresses immune response to tumors.

Antigen presentation on glioblastoma cells was also transformed by interactions with macrophages. These antigens were associated with Rho GTPase, a signaling protein that belongs to Ras, a class of proteins that is mutated in 30 percent of all cancers. Changes in Rho GTPase expression predispose cells to developing hallmark traits of cancer, such as prolonged cell longevity, abnormal growth, and metastasis. Antigen profiles of co-cultured glioblastoma cells revealed over 40 Rho GTPase-associated antigens with increased expression compared to tumor cells cultured in isolation.

Researchers compared antigen expression changes in co-cultured macrophage and glioblastoma cells to immunopeptidome profiles of mouse models and human tumor samples, finding that patterns observed in cell culture translated to animal models and, potentially, to patients.

Researchers selected six antigens showing increased expression in either glioblastoma cells or macrophages to test as therapeutic targets, developing an mRNA-based immunostimulatory therapy for each antigen. After treating mice with glioblastoma, tumors showed significantly slowed growth overall and, in a few cases, were completely eradicated. 

In future work, the team plans to use their immunopeptidome profiling techniques to characterize co-cultured dendritic cells, which retrieve proteins from cancer cells and presents them to T cells as antigens, as well as to explore antigen presentation of cells in live models of glioblastoma.

“This study demonstrates the promise of profiling cell surface antigens,” says Cui. “With quantitative accuracy and cell type resolution, our approach could be used to design improved immunotherapies against many cancer types and other diseases,” says Cui.

This work was supported, in part, by the National Cancer Institute (NCI) and the MIT Center for Precision Cancer Medicine. 

A massive wave of public comments just told the U.S. Patent and Trademark Office (USPTO): don’t shut the public out of patent review.

EFF submitted its own formal comment opposing the USPTO’s proposed rules, and more than 4,000 supporters added their voices—an extraordinary response for a technical, fast-moving rulemaking. We comprised more than one-third of the 11,442 comments submitted. The message is unmistakable: the public wants a meaningful way to challenge bad patents, and the USPTO should not take that away.

The Public Doesn’t Want To Bury Patent Challenges

These thousands of submissions do more than express frustration. They demonstrate overwhelming public interest in preserving inter partes review (IPR), and undermine any broad claim that the USPTO’s proposal reflects public sentiment. 

Comments opposing the rulemaking include many small business owners who have been wrongly accused of patent infringement, by both patent trolls and patent-abusing competitors. They also include computer science experts, law professors, and everyday technology users who are simply tired of patent extortion—abusive assertions of low-quality patents—and the harm it inflicts on their work, their lives, and the broader U.S. economy. 

The USPTO exists to serve the public. The volume and clarity of this response make that expectation impossible to ignore.

EFF’s Comment To USPTO

In our filing, we explained that the proposed rules would make it significantly harder for the public to challenge weak patents. That undercuts the very purpose of IPR. The proposed rules would pressure defendants to give up core legal defenses, allow early or incomplete decisions to block all future challenges, and create new opportunities for patent owners to game timing and shut down PTAB review entirely.

Congress created IPR to allow the Patent Office to correct its own mistakes in a fair, fast, expert forum. These changes would take the system backward. 

A Broad Coalition Supports IPR

A wide range of groups told the USPTO the same thing: don’t cut off access to IPR.

Open Source and Developer Communities 

The Linux Foundation submitted comments and warned that the proposed rules “would effectively remove IPRs as a viable mechanism for challenges to patent validity,” harming open-source developers and the users that rely on them. Github wrote that the USPTO proposal would increase “litigation risk and costs for developers, startups, and open source projects.” And dozens of individual software developers described how bad patents have burdened their work. 

Patent Law Scholars

A group of 22 patent law professors from universities across the country said the proposed rule changes “would violate the law, increase the cost of innovation, and harm the quality of patents.” 

Patient Advocates

Patients for Affordable Drugs warned in their filing that IPR is critical for invalidating wrongly granted pharmaceutical patents. When such patents are invalidated, studies have shown “cardiovascular medications have fallen 97% in price, cancer drugs dropping 80-98%, and treatments for opioid addiction becom[e] 50% more affordable.” In addition, “these cases involved patents that had evaded meaningful scrutiny in district court.” 

Small Businesses 

Hundreds of small businesses weighed in with a consistent message: these proposed rules would hit them hardest. Owners and engineers described being targeted with vague or overbroad patents they cannot afford to litigate in court, explaining that IPR is often the only realistic way for a small firm to defend itself. The proposed rules would leave them with an impossible choice—pay a patent troll, or spend money they don’t have fighting in federal court. 

What Happens Next

The USPTO now has thousands of comments to review. It should listen. Public participation must be more than a box-checking exercise. It is central to how administrative rulemaking is supposed to work.

Congress created IPR so the public could help correct bad patents without spending millions of dollars in federal court. People across technical, academic, and patient-advocacy communities just reminded the agency why that matters. 

We hope the USPTO reconsiders these proposed rules. Whatever happens, EFF will remain engaged and continue fighting to preserve  the public’s ability to challenge bad patents. 

I have long maintained that smart contracts are a dumb idea: that a human process is actually a security feature.

Here’s some interesting research on training AIs to automatically exploit smart contracts:

AI models are increasingly good at cyber tasks, as we’ve written about before. But what is the economic impact of these capabilities? In a recent MATS and Anthropic Fellows project, our scholars investigated this question by evaluating AI agents’ ability to exploit smart contracts on Smart CONtracts Exploitation benchmark (SCONE-bench)­a new benchmark they built comprising 405 contracts that were actually exploited between 2020 and 2025. On contracts exploited after the latest knowledge cutoffs (June 2025 for Opus 4.5 and March 2025 for other models), Claude Opus 4.5, Claude Sonnet 4.5, and GPT-5 developed exploits collectively worth $4.6 million, establishing a concrete lower bound for the economic harm these capabilities could enable. Going beyond retrospective analysis, we evaluated both Sonnet 4.5 and GPT-5 in simulation against 2,849 recently deployed contracts without any known vulnerabilities. Both agents uncovered two novel zero-day vulnerabilities and produced exploits worth $3,694, with GPT-5 doing so at an API cost of $3,476. This demonstrates as a proof-of-concept that profitable, real-world autonomous exploitation is technically feasible, a finding that underscores the need for proactive adoption of AI for defense...

Antibody treatments for cancer and other diseases are typically delivered intravenously, because of the large volumes that are needed per dose. This means the patient has to go to a hospital for every treatment, where they may spend hours receiving the infusion.

MIT engineers have now taken a major step toward reformulating antibodies so that they can be injected using a standard syringe. The researchers found a way to create solid particles of highly concentrated antibodies, suspended in a solution. These particles carry enough antibodies that only about 2 milliliters of solution would be needed per dose.

This advance could make it much easier for patients to receive antibody treatments, and could make treatment more accessible for patients who have difficulty coming into a hospital, including older people.

“As the global population ages, making the treatment process more convenient and accessible for those populations is something that needs to be addressed,” says Talia Zheng, an MIT graduate student who is the lead author of the new study.

Patrick Doyle, the Robert T. Haslam Professor of Chemical Engineering, is the senior author of the open-access paper, which appears in Advanced Materials. MIT graduate student Lucas Attia and Janet Teng ’25 are also authors of the study.

Highly concentrated antibodies

Therapeutic antibody drugs such as rituximab, which is used to treat some cancers, consist of antibodies suspended in a water-based solution. In addition to cancers, antibodies are also used to treat infectious diseases, as well as autoimmune disorders such as rheumatoid arthritis, inflammatory bowel disease, and multiple sclerosis.

Because the antibody solutions are formulated at low concentrations (10 to 30 milligrams of antibody per milliliter of solution), patients need to be given at least 100 milliliters per dose, which is much too large to be injected using a standard syringe. To decrease this volume to the point where it could be injected, the antibody concentration would need to be at least 300 milligrams per milliliter, but that would make the solution much too thick to be injected.

“You can’t concentrate existing formulations to these concentrations,” Doyle says. “They’ll be very viscous and will exceed the force threshold of what you can inject into a patient.”

In 2023, Doyle’s lab developed a way to generated highly concentrated antibody formulations by encapsulating them into hydrogel particles. However, that process requires centrifugation, a step that would be difficult to scale up for manufacturing.

In their new study, the researchers took a different approach that allows them to create droplets suspended in an emulsion, similar to oil and vinegar. In this case, droplets containing antibodies dissolved in a watery solution are suspended in an organic solvent called pentanol.

These droplets can then be dehydrated, leaving behind highly concentrated solid antibodies — about 360 milligrams of antibody per milliliter of solution. These particles also include a small amount of polyethylene glycol (PEG), a polymer that helps stabilize the particles.

Once these solid particles form, the organic solvent surrounding them is removed and replaced with an aqueous solution (water containing dissolved salts and small amount of stabilizing polymer), similar to the solution now used to infuse therapeutic antibodies.

This assembly process can be done rapidly using a microfluidic setup and does not require centrifugation, which should allow it to be scaled up much more easily using emulsification devices compliant with GMP (good manufacturing practice) regulations.

“Our first approach was a bit brute force, and when we were developing this new approach, we said to it’s got to be simple if it’s going to be better and scalable,” Doyle says.

Injectable particles

The researchers showed that they could control the size of the particles — from about 60 to 200 microns in diameter — by changing the flow rate of the solutions that make up the droplets.

Using particles 100 microns in diameter, they tested the injectability of the solution using a mechanical force tester. Those studies showed that the force needed to push the plunger of a syringe containing the particle solution was less than 20 newtons.

“That is less than half of the maximum acceptable force that people usually try to aim for, so it’s very injectable,” Zheng says.

Using a 2-milliliter syringe, a typical size for subcutaneous injections, more than 700 milligrams of the target antibody could be given at once — enough for most therapeutic applications. The researchers also showed that their formulations remained stable under refrigeration for at least four months.

The researchers now plan to test their antibody particles for therapeutic applications in animal models. They are also working on scaling up the manufacturing process, so they can make enough for large-scale testing.

The research was funded by the MIT Undergraduate Research Opportunities Program and the U.S. Department of Energy.

Lisa Su ’90, SM ’91, PhD ’94, a leading executive in the semiconductor industry and head of the company Advanced Micro Devices (AMD), will deliver the address at the OneMIT Commencement Ceremony on Thursday, May 28.

As chair and CEO of AMD, Su has transformed the company, which is now a global leader in high-performance and AI computing. In addition to designing industry-leading CPUs and the specialized GPUs that enable AI applications, AMD technology is the foundation of many of the world’s most advanced supercomputers and high-performance computing systems. The company continues to work on next-generation hardware and open software that will accelerate the adoption of AI, which Su has described as the most transformational technology of our time.

Su has maintained a close relationship with MIT since her days as a student. She was the speaker at the 2017 doctoral hooding ceremony, and in 2018 she established the Lisa Su Fellowship Fund. She served on the Electrical Engineering and Computer Science Visiting Committee for 10 years. In 2022, Building 12, which houses MIT.nano, was named in her honor.

“Long before she led the spectacular turnaround of AMD and lent her name to MIT’s world-class nano facility, Lisa Su was an MIT student who inspired and mentored her classmates. During her PhD studies, she created instructions that guided generations of student researchers in using some of the Institute’s most advanced equipment,” says MIT President Sally Kornbluth. “Lisa is renowned for her intellectual rigor, boldness, and originality, and we're absolutely thrilled that she has agreed to deliver the Commencement address to our graduates this year.”

“MIT has always held a special place in my life and career, and I’m thrilled to accept the invitation to speak at Commencement,” Su says. “The Class of 2026 will be graduating at an exciting time, as AI transforms our world and expands what is possible, and I look forward to celebrating them as they prepare to share their skills and ideas with the world.”

Born in Taiwan, Su grew up in Queens, New York. After earning bachelor’s, master’s, and doctoral degrees in electrical engineering from MIT, she worked at Texas Instruments, IBM, and Freescale Semiconductor, then joined AMD in 2012. In her current position, Su is a member of a small group: Only about 10 percent of Fortune 500 companies have female CEOs.

“Lisa Su has embraced MIT’s ‘mind and hand’ motto over the course of her career, first with important scientific discoveries in semiconductor design and engineering, and later as an extraordinary business executive leading the delivery of innovative products that play an essential role in the modern digital economy. We are very fortunate that she has agreed to share some of the lessons learned on her journey,” says Jim Poterba, the Mitsui Professor of Economics and chair of the Commencement Committee.

“Dr. Lisa Su is an inspiration to the MIT community for the way she combines exceptional engineering and leadership with meaningful, far-reaching impact in computing and countless other fields,” senior class president Heba Hussein says. “Her journey embodies the spirit of MIT, and the Class of 2026 is incredibly excited to welcome her at Commencement as we step into the world carrying the same MIT values!”

“I am excited to hear from someone that I know we can all learn something from. I think all MIT students respect the ‘lock-in’ that must have been required to achieve all that she has, with AMD and beyond,” says Alice Hall, president of the Undergraduate Association.

“Dr. Su is a world leader in manufacturing technologies and personifies MIT's values. As an alum, she has shared many experiences with current students, and I look forward to hearing about how these experiences shaped her successful career,” says Teddy Warner, president of the Graduate Student Council.

Su has received many honors including two named for MIT alumni: the Global Semiconductor Association’s Dr. Morris Chang Exemplary Leadership Award and the Robert N. Noyce Medal. She was named TIME’s 2024 CEO of the Year and has been recognized as one of TIME’s 100 Most Influential People and Fortune's Most Powerful People in Business. She received the 2024 Bower Award for Business Leadership and the Distinguished Leadership Award from the Committee for Economic Development (CED). Su is a member of the American Academy of Arts and Sciences and the National Academy of Engineering.

Su joins notable recent MIT Commencement speakers including science communicator Hank Green (2025); inventor and entrepreneur Noubar Afeyan (2024); YouTuber and inventor Mark Rober (2023); Director-General of the World Trade Organization Ngozi Okonjo-Iweala (2022); lawyer and social justice activist Bryan Stevenson (2021); and retired U.S. Navy four-star admiral William McRaven (2020). 

In announcing the U.S. had seized an oil tanker off Venezuela on Wednesday, President Donald Trump said of its crude, “We’ll keep it, I guess.”

The young people argued in a petition filed with the Montana Supreme Court that lawmakers are flouting a 2024 ruling that determined state energy laws infringed on their constitutional rights.

Large power consumers will need 225 gigawatts of electricity over the next five years, testing the state's abililty to quickly add generation.

The effects were stronger among children living in cities, poorer households and places with less access to clean water, new research finds.