Usha Lee McFarling named director of the Knight Science Journalism Program

The Knight Science Journalism Program (KSJ) at MIT has announced that Usha Lee McFarling, national science correspondent for STAT and former KSJ Fellow, will be joining the team in August as their next director.

As director, McFarling will play a central role in helping to manage KSJ — an elite mid-career fellowship program that brings prominent science journalists from around the world for 10 months of study and intellectual exploration at MIT, Harvard University, and other institutions in the Boston area.

“I’m eager to take the helm during this critical time for science journalism, a time when journalism is under attack both politically and economically and misinformation — especially in areas of science and health — is rife,” says McFarling. “My goal is for the program to find even more ways to support our field and its practitioners as they carry on their important work.”

McFarling is a veteran science writer, most recently working for STAT News. She previously reported for the Los Angeles Times, The Boston Globe, Knight Ridder Washington Bureau, and the San Antonio Light, and was a Knight Science Journalism Fellow in 1992-93. McFarling graduated from Brown University with a degree in biology in 1988 and later earned a master’s degree in biological psychology from the University of California at Berkeley.

Her work on the diseased state of the world’s oceans earned the 2007 Pulitzer Prize for explanatory journalism and a Polk Award, among others. Her coverage of health disparities at STAT has earned an Edward R. Murrow award, and awards from the Association of Health Care Journalists, and the Asian American Journalists Association. In 2024, she was awarded the Victor Cohn prize for excellence in medical science reporting and the Bernard Lo, MD award in bioethics.

McFarling will succeed director Deborah Blum, who served as director for 10 years. Blum, also a Pulitzer-prize winning journalist and the bestselling author of six books, is retiring to return to a full-time writing career. She will join the board of Undark, a magazine she helped found while at KSJ, and continue as a board member of the Council for the Advancement of Science Writing and the Burroughs Wellcome Fund, among others.

“It’s been an honor to serve as director of the Knight Science Journalism program for the past 10 years and a pleasure to be able to support the important work that science journalists do,” Blum says. “And I know that under the direction of Usha McFarling — who brings such talent and intelligence to the job — that KSJ will continue to grow and thrive in all the best ways.”

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With AI, researchers predict the location of virtually any protein within a human cell

A protein located in the wrong part of a cell can contribute to several diseases, such as Alzheimer’s, cystic fibrosis, and cancer. But there are about 70,000 different proteins and protein variants in a single human cell, and since scientists can typically only test for a handful in one experiment, it is extremely costly and time-consuming to identify proteins’ locations manually.

A new generation of computational techniques seeks to streamline the process using machine-learning models that often leverage datasets containing thousands of proteins and their locations, measured across multiple cell lines. One of the largest such datasets is the Human Protein Atlas, which catalogs the subcellular behavior of over 13,000 proteins in more than 40 cell lines. But as enormous as it is, the Human Protein Atlas has only explored about 0.25 percent of all possible pairings of all proteins and cell lines within the database.

Now, researchers from MIT, Harvard University, and the Broad Institute of MIT and Harvard have developed a new computational approach that can efficiently explore the remaining uncharted space. Their method can predict the location of any protein in any human cell line, even when both protein and cell have never been tested before.

Their technique goes one step further than many AI-based methods by localizing a protein at the single-cell level, rather than as an averaged estimate across all the cells of a specific type. This single-cell localization could pinpoint a protein’s location in a specific cancer cell after treatment, for instance.

The researchers combined a protein language model with a special type of computer vision model to capture rich details about a protein and cell. In the end, the user receives an image of a cell with a highlighted portion indicating the model’s prediction of where the protein is located. Since a protein’s localization is indicative of its functional status, this technique could help researchers and clinicians more efficiently diagnose diseases or identify drug targets, while also enabling biologists to better understand how complex biological processes are related to protein localization.

“You could do these protein-localization experiments on a computer without having to touch any lab bench, hopefully saving yourself months of effort. While you would still need to verify the prediction, this technique could act like an initial screening of what to test for experimentally,” says Yitong Tseo, a graduate student in MIT’s Computational and Systems Biology program and co-lead author of a paper on this research.

Tseo is joined on the paper by co-lead author Xinyi Zhang, a graduate student in the Department of Electrical Engineering and Computer Science (EECS) and the Eric and Wendy Schmidt Center at the Broad Institute; Yunhao Bai of the Broad Institute; and senior authors Fei Chen, an assistant professor at Harvard and a member of the Broad Institute, and Caroline Uhler, the Andrew and Erna Viterbi Professor of Engineering in EECS and the MIT Institute for Data, Systems, and Society (IDSS), who is also director of the Eric and Wendy Schmidt Center and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS). The research appears today in Nature Methods.

Collaborating models

Many existing protein prediction models can only make predictions based on the protein and cell data on which they were trained or are unable to pinpoint a protein’s location within a single cell.

To overcome these limitations, the researchers created a two-part method for prediction of unseen proteins’ subcellular location, called PUPS.

The first part utilizes a protein sequence model to capture the localization-determining properties of a protein and its 3D structure based on the chain of  amino acids that forms it.

The second part incorporates an image inpainting model, which is designed to fill in missing parts of an image. This computer vision model looks at three stained images of a cell to gather information about the state of that cell, such as its type, individual features, and whether it is under stress.

PUPS joins the representations created by each model to predict where the protein is located within a single cell, using an image decoder to output a highlighted image that shows the predicted location.

“Different cells within a cell line exhibit different characteristics, and our model is able to understand that nuance,” Tseo says.

A user inputs the sequence of amino acids that form the protein and three cell stain images — one for the nucleus, one for the microtubules, and one for the endoplasmic reticulum. Then PUPS does the rest.

A deeper understanding

The researchers employed a few tricks during the training process to teach PUPS how to combine information from each model in such a way that it can make an educated guess on the protein’s location, even if it hasn’t seen that protein before.

For instance, they assign the model a secondary task during training: to explicitly name the compartment of localization, like the cell nucleus. This is done alongside the primary inpainting task to help the model learn more effectively.

A good analogy might be a teacher who asks their students to draw all the parts of a flower in addition to writing their names. This extra step was found to help the model improve its general understanding of the possible cell compartments.

In addition, the fact that PUPS is trained on proteins and cell lines at the same time helps it develop a deeper understanding of where in a cell image proteins tend to localize.

PUPS can even understand, on its own, how different parts of a protein’s sequence contribute separately to its overall localization.

“Most other methods usually require you to have a stain of the protein first, so you’ve already seen it in your training data. Our approach is unique in that it can generalize across proteins and cell lines at the same time,” Zhang says.

Because PUPS can generalize to unseen proteins, it can capture changes in localization driven by unique protein mutations that aren’t included in the Human Protein Atlas.

The researchers verified that PUPS could predict the subcellular location of new proteins in unseen cell lines by conducting lab experiments and comparing the results. In addition, when compared to a baseline AI method, PUPS exhibited on average less prediction error across the proteins they tested.

In the future, the researchers want to enhance PUPS so the model can understand protein-protein interactions and make localization predictions for multiple proteins within a cell. In the longer term, they want to enable PUPS to make predictions in terms of living human tissue, rather than cultured cells.

This research is funded by the Eric and Wendy Schmidt Center at the Broad Institute, the National Institutes of Health, the National Science Foundation, the Burroughs Welcome Fund, the Searle Scholars Foundation, the Harvard Stem Cell Institute, the Merkin Institute, the Office of Naval Research, and the Department of Energy.

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Particles carrying multiple vaccine doses could reduce the need for follow-up shots

Around the world, 20 percent of children are not fully immunized, leading to 1.5 million child deaths each year from diseases that are preventable by vaccination. About half of those underimmunized children received at least one vaccine dose but did not complete the vaccination series, while the rest received no vaccines at all.

To make it easier for children to receive all of their vaccines, MIT researchers are working to develop microparticles that can release their payload weeks or months after being injected. This could lead to vaccines that can be given just once, with several doses that would be released at different time points.

In a study appearing today in the journal Advanced Materials, the researchers showed that they could use these particles to deliver two doses of diphtheria vaccine — one released immediately, and the second two weeks later. Mice that received this vaccine generated as many antibodies as mice that received two separate doses two weeks apart.

The researchers now hope to extend those intervals, which could make the particles useful for delivering childhood vaccines that are given as several doses over a few months, such as the polio vaccine.

“The long-term goal of this work is to develop vaccines that make immunization more accessible — especially for children living in areas where it’s difficult to reach health care facilities. This includes rural regions of the United States as well as parts of the developing world where infrastructure and medical clinics are limited,” says Ana Jaklenec, a principal investigator at MIT’s Koch Institute for Integrative Cancer Research.

Jaklenec and Robert Langer, the David H. Koch Institute Professor at MIT, are the senior authors of the study. Linzixuan (Rhoda) Zhang, an MIT graduate student who recently completed her PhD in chemical engineering, is the paper’s lead author.

Self-boosting vaccines

In recent years, Jaklenec, Langer, and their colleagues have been working on vaccine delivery particles made from a polymer called PLGA. In 2018, they showed they could use these types of particles to deliver two doses of the polio vaccine, which were released about 25 days apart.

One drawback to PLGA is that as the particles slowly break down in the body, the immediate environment can become acidic, which may damage the vaccine contained within the particles.

The MIT team is now working on ways to overcome that issue in PLGA particles and is also exploring alternative materials that would create a less acidic environment. In the new study, led by Zhang, the researchers decided to focus on another type of polymer, known as polyanhydride.

“The goal of this work was to advance the field by exploring new strategies to address key challenges, particularly those related to pH sensitivity and antigen degradation,” Jaklenec says.

Polyanhydrides, biodegradable polymers that Langer developed for drug delivery more than 40 years ago, are very hydrophobic. This means that as the polymers gradually erode inside the body, the breakdown products hardly dissolve in water and generate a much less acidic environment.

Polyanhydrides usually consist of chains of two different monomers that can be assembled in a huge number of possible combinations. For this study, the researchers created a library of 23 polymers, which differed from each other based on the chemical structures of the monomer building blocks and the ratio of the two monomers that went into the final product.

The researchers evaluated these polymers based on their ability to withstand temperatures of at least 104 degrees Fahrenheit (40 degrees Celsius, or slightly above body temperature) and whether they could remain stable throughout the process required to form them into microparticles.

To make the particles, the researchers developed a process called stamped assembly of polymer layers, or SEAL. First, they use silicon molds to form cup-shaped particles that can be filled with the vaccine antigen. Then, a cap made from the same polymer is applied and sealed using heat. Polymers that proved too brittle or didn’t seal completely were eliminated from the pool, leaving six top candidates.

The researchers used those polymers to design particles that would deliver diphtheria vaccine two weeks after injection, and gave them to mice along with vaccine that was released immediately. Four weeks after the initial injection, those mice showed comparable levels of antibodies to mice that received two doses two weeks apart.

Extended release

As part of their study, the researchers also developed a machine-learning model to help them explore the factors that determine how long it takes the particles to degrade once in the body. These factors include the type of monomers that go into the material, the ratio of the monomers, the molecular weight of the polymer, and the loading capacity or how much vaccine can go into the particle.

Using this model, the researchers were able to rapidly evaluate nearly 500 possible particles and predict their release time. They tested several of these particles in controlled buffers and showed that the model’s predictions were accurate.

In future work, this model could also help researchers to develop materials that would release their payload after longer intervals — months or even years. This could make them useful for delivering many childhood vaccines, which require multiple doses over several years.

“If we want to extend this to longer time points, let’s say over a month or even further, we definitely have some ways to do this, such as increasing the molecular weight or the hydrophobicity of the polymer. We can also potentially do some cross-linking. Those are further changes to the chemistry of the polymer to slow down the release kinetics or to extend the retention time of the particle,” Zhang says.

The researchers now hope to explore using these delivery particles for other types of vaccines. The particles could also prove useful for delivering other types of drugs that are sensitive to acidity and need to be given in multiple doses, they say.

“This technology has broad potential for single-injection vaccines, but it could also be adapted to deliver small molecules or other biologics that require durability or multiple doses. Additionally, it can accommodate drugs with pH sensitivities,” Jaklenec says.

The research was funded, in part, by the Koch Institute Support (core) Grant from the National Cancer Institute.

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Class pairs students with military officers to build mission-critical solutions

On a recent Friday afternoon, Marine Corps General and U.S. Congressman Jake Auchincloss stood in the front of a crowded MIT classroom in Building 1 and made his case for modernizing America’s military to counter the threat from China. Part of his case involved shifting resources away from the U.S. Army to bolster the Marines, Navy, and Air Force.

When it was time for questions, several hands shot up. One person took exception to Auchincloss’ plans for the Army, although he admitted his views were influenced by the fact that he was an active member of the Army’s Special Forces. Another person had a question about the future of wartime technology. Again, the questioner had some personal experience: He sits on the board of a Ukrainian drone-manufacturing company. Next up was an MIT student with a question about artificial intelligence.

Course 15.362/6.9160 (Engineering Innovation: Global Security Systems) is not your typical MIT class. It teaches students about the most pressing problems in global security and challenges them to build functioning prototypes over the course of one whirlwind semester. Along the way, students hear from high-ranking members of the military, MIT professors, government officials, startup founders, and others to learn about the realities of combat and how best to create innovative solutions.

“As far as I know, this is the only class in the world that works in this way,” says Gene Keselman MBA ’17, a lecturer in the MIT Sloan School of Management and a colonel in the U.S. Air Force Reserves who helped start the class. “There are other classes trying to do something similar, but they use intermediaries. In this course, the Navy SEALs are in the classroom working directly with the students. By teaching students in this way, we’re giving them exposure to something they’d never otherwise be exposed to.”

In the beginning of the semester, students split into interdisciplinary groups that feature both undergraduate and graduate students. Each group is assigned mentors with deep military experience. From there, students learn to take a problem, map out a set of possible solutions, and pitch their prototypes to the active members of the armed services they’re trying to help.

They get feedback on their ideas and iterate as they go through a series of presentation milestones throughout the semester.

“The outcomes are twofold,” says A.J. Perez ’13, MEng ’14, PhD ’23, a lecturer in the MIT School of Engineering and a research scientist with the Office of Innovation, who built the course’s engineering design curriculum. “There are the prototypes, which could have real impact on war fighters, and then there are the learnings students get by going through the process of defining a problem and building a prototype. The prototype is important, but the process of the class leads to skills that are transferable to a multitude of other domains.”

The class’s organizers say although the course is only in its second year, it aligns with MIT’s long legacy of working alongside the military.

“MIT has these incredibly fruitful relationships with the Department of Defense going back to World War II,” says Keselman. “We developed advanced radar systems that helped win the war and launched the military-industrial complex, including organizations like MIT Lincoln Laboratory and MITRE. It’s in our ethos, it’s in our culture, and this is another extension of that. This is another way for MIT to lead in tough tech and work on the world’s hardest problems. We couldn’t do this class in another university in this country.”

Tapping into student interest

Like many things at MIT, the class was inspired by a hackathon. For several years, college students in the U.S. Armed Forces’ Reserve Officers’ Training Corps (ROTC) program came to MIT from across the country for a weekend hackathon focused on solving specific military problems.

Last year, Keselman, Perez, and others decided to create the class to give MIT’s ROTC cadets more time to work on the projects and give them the opportunity to earn course credit. But when Keselman and Perez announced a class geared toward solving problems in the armed forces, many non-ROTC MIT students enrolled.

“We realized there was a lot of interest in national security at MIT beyond the ROTC cadets,” Keselman explains. “National security is obviously important to a lot of people, but it also offers super interesting problems you can’t find anywhere else. I think that attracted students from all over MIT.”

About 25 students enrolled the first year to work on a problem that prevented U.S. Navy SEALs from bringing lithium-ion batteries onto submarines. This year, the organizers who include senior faculty members Fiona Murray, Sertac Karaman, and Vladimir Bulovic, couldn’t fit everyone who showed up, so they expanded to room 1-190, a lecture hall. They also added graduate-level credits and were more prepared for student interest.

More than 70 students registered this year from 15 different MIT departments, Harvard College, the Harvard Business School, and the Harvard Kennedy School. Student groups contain undergraduates, graduates, engineers, and business students. Many have military experience, and each group has access to mentors from places including the Navy, Air Force, Special Operations, and the Massachusetts State Police.

“Last year a student said, ‘This class is weird, and that’s exactly why it needs to stick around,’” Keselman says. “It is weird. It’s not normal for this many disciplines to come together, to have a Congressman showing up, Navy Seals, and members of the Army’s Delta Force all sitting in a room. Some are active-duty students, some are mentors, but it’s an incredible melting pot. I think it’s exactly what MIT embodies.”

From projects to military programs

This year’s class project challenges students to develop countermeasures for autonomous drone systems, which either travel by air or sea. Over the course of the semester, teams have built solutions that achieve early drone detection, categorization, and countermeasures. The solutions also must integrate AI and consider domestic manufacturing capabilities and supply chains.

One group is using sensors to detect the auditory signature of drones in the air. In the class, they gave a live demo that would only signal a threat when it detected a certain steady pitch associated with the electric motor of an air drone.

“Nothing motivates MIT students like a problem in the real world that they know really matters,” Perez says. “At the core of this year’s problem is how we protect a human from a drone attack. They take the process seriously.”

Last year, the military funded a $2 million program to further develop one student’s project for the U.S. Special Operations Command (USSOCOM).

“Students gain important skills to become product design engineers,” Perez says. “The hard results are inventions, prototypes, academic papers, and proposals to continue developing the technology.”

Organizers have also talked about extending the class into a year-long program that would allow the teams to build their projects into real products in partnership with groups at places like Lincoln Laboratory.

“This class is spreading the seeds of collaboration between academia and government,” Keselman says. “It’s a true partnership as opposed to just a funding program. Government officials come to MIT and sit in the classroom and see what’s actually happening here — and they rave about how impressive all the work is.”

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3 Questions: Making the most of limited data to boost pavement performance

Pavements form the backbone of our built environment. In the United States, almost 2.8 million lane-miles, or about 4.6 million lane-kilometers, are paved. They take us to work or school, take goods to their destinations, and much more.

To secure a more sustainable future, we must take a careful look at the long-term performance and environmental impacts of our pavements. Haoran Li, a postdoc at the MIT Concrete Sustainability Hub and the Department of Civil and Environmental Engineering, is deeply invested in studying how to give stakeholders the information and tools they need to make informed pavement decisions with the future in mind. Here, he discusses life-cycle assessments for pavements as well as research from MIT in addressing pavement sustainability.

Q: What is life-cycle assessment, and why does it matter for pavements?

A: Life-cycle assessment (LCA) is a method that helps us holistically assess the environmental impacts of products and systems throughout their life cycle — everything from the impacts of raw materials to construction, use, maintenance, and repair, and finally decommissioning. For pavements, up to 78 percent of the life-cycle impact comes from the use phase, with the majority stemming from vehicle fuel use impacted by pavement characteristics, such as stiffness and smoothness. This phase also includes the sunlight reflected by pavements: Lighter, more reflective pavement bounces heat back into the atmosphere instead of absorbing it, which can help keep nearby buildings and streets cooler, At the same time, there are positive use phase impacts like carbon uptake — the natural process by which cement-based products like concrete roads and infrastructure sequester CO₂ [carbon dioxide] from the atmosphere. Due to the sheer area of our pavements, they offer a great potential for the sustainability solution. Unlike many decarbonization solutions, pavements are managed by government agencies and influence the emissions from vehicles and surrounding buildings, allowing for a coordinated push toward sustainability through better materials, designs, and maintenance.

Q: What are the gaps in current pavement life-cycle assessment methods and tools and what has the MIT Concrete Sustainability Hub done to address them so far?

A: A key gap is the complexity of performing pavement LCA. Practitioners should assess both the long-term structural performance and environmental impacts of paving materials, considering the pavements’ interactions with the built environment. Another key gap is the great uncertainty associated with pavement LCA. Since pavements are designed to last for decades, it is necessary to handle the inherent uncertainty through their long-term performance evaluations.

To tackle these challenges, the MIT Concrete Sustainability Hub (CSHub) developed an innovative method and practical tools that address data intensity and uncertainty while offering context-specific and probabilistic LCA strategies. For instance, we demonstrated that it is possible to achieve meaningful results on the environmentally preferred pavement alternatives while reducing data collection efforts by focusing on the most influential and least variable parameters. By targeting key variables that significantly impact the pavement’s life cycle, we can streamline the process and still obtain robust conclusions. Overall, the efforts of the CSHub aim to enhance the accuracy and efficiency of pavement LCAs, making them better aligned with real-world conditions and more manageable in terms of data requirements.

Q: How does the MIT Concrete Sustainability Hub’s new streamlined pavement life-cycle assessment method improve on previous designs?

A: The CSHub recently developed a new framework to streamline both probabilistic and comparative LCAs for pavements. Probabilistic LCA accounts for randomness and variability in data, while comparative LCA allows the analysis of different options simultaneously to determine the most sustainable choice.

One key innovation is the use of a structured data underspecification approach, which prioritizes the data collection efforts. In pavement LCA, underspecifying can reduce the overall data collection burden by up to 85 percent, allowing for a reliable decision-making process with minimal data. By focusing on the most critical elements, we can still reach robust conclusions without the need for extensive data collection.

To make this framework practical and accessible, it is being integrated into an online LCA software tool. This tool facilitates use by practitioners, such as departments of transportation and metropolitan planning organizations. It helps them identify choices that lead to the highest-performing, longest-lasting, and most environmentally friendly pavements. Some of these solutions could include incorporating low-carbon concrete mixtures, prioritizing long-lasting treatment actions, and optimizing the design of pavement geometry to reduce life-cycle greenhouse gas emissions.

Overall, the CSHub’s new streamlined pavement LCA method significantly improves the efficiency and accessibility of conducting pavement LCAs, making it easier for stakeholders to make informed decisions that enhance pavement performance and sustainability.

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Steven Truong ’20 named 2025 Knight-Hennessy Scholar

MIT alumnus Steven Troung ’20 has been awarded a 2025 Knight-Hennessy Scholarship and will join the eighth cohort of the prestigious fellowship. Knight-Hennessy Scholars receive up to three years of financial support for graduate studies at Stanford University.

Knight-Hennessy Scholars are selected for their independence of thought, purposeful leadership, and civic mindset. Troung is dedicated to making scientific advances in metabolic disorders, specifically diabetes, a condition that has affected many of his family members.

Truong, the son of Vietnamese refugees, originally hails from Minneapolis and graduated from MIT in 2020 with bachelor’s degrees in biological engineering and creative writing. During his time at MIT, Truong conducted research on novel diabetes therapies with professors Daniel Anderson and Robert Langer at the Koch Institute for Integrative Cancer Research and with Professor Douglas Lauffenburger in the Department of Biological Engineering.

Troung also founded a diabetes research project in Vietnam and co-led Vietnam’s largest genome-wide association study with physicians at the University of Medicine and Pharmacy in Ho Chi Minh City, where the team investigated the genetic determinants of Type 2 diabetes.

In his senior year at MIT, Truong won a Marshall Scholarship for post-graduate studies in the U.K. As a Marshall Scholar, he completed an MPhil in computational biology at Cambridge University and an MA in creative writing at Royal Holloway, University of London. Troung is currently pursuing an MD and a PhD in biophysics at the Stanford School of Medicine.

In addition to winning a Knight-Hennessy Scholarship and the Marshall Scholarship, Truong was the recipient of a 2019-20 Goldwater Scholarship and a 2023 Paul and Daisy Soros Fellowship for New Americans.

Students interested in applying to the Knight-Hennessy Scholars program can contact Kim Benard, associate dean of distinguished fellowships in Career Advising and Professional Development. 

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Drug injection device wins MIT $100K Competition

The winner of this year’s MIT $100K Entrepreneurship Competition is helping advanced therapies reach more patients faster with a new kind of drug-injection device.

CoFlo Medical says its low-cost device can deliver biologic drugs more than 10 times faster than existing methods, accelerating the treatment of a range of conditions including cancers, autoimmune diseases, and infectious diseases.

“For patients battling these diseases, every hour matters,” said Simon Rufer SM ’22 in the winning pitch. “Biologic drugs are capable of treating some of the most challenging diseases, but their administration is unacceptably time-consuming, infringing on the freedom of the patient and effectively leaving them tethered to their hospital beds. The requirement of a hospital setting also makes biologics all but impossible in remote and low-access areas.”

Today, biologic drugs are mainly delivered through intravenous fusions, requiring patients to sit in hospital beds for hours during each delivery. That’s because many biologic drugs are too viscous to be pushed through a needle. CoFlo’s device enables quick injections of biologic drugs no matter how viscous. It works by surrounding the viscous drug with a second, lower-viscosity fluid.

“Imagine trying to force a liquid as viscous as honey through a needle: It’s simply not possible,” said Rufer, who is currently a PhD candidate in the Department of Mechanical Engineering. “Over the course of six years of research and development at MIT, we’ve overcome a myriad of fluidic instabilities that have otherwise made this technology impossible. We’ve also patented the fundamental inner workings of this device.”

Rufer made the winning pitch to a packed Kresge Auditorium that included a panel of judges on May 12. In a video, he showed someone injecting biologic drugs using CoFlo’s device using one hand.

Rufer says the second fluid in the device could be the buffer of the drug solution itself, which wouldn’t alter the drug formulation and could potentially expedite the device’s approval in clinical trials. The device can also easily be made using existing mass manufacturing processes, which will keep the cost low.

In laboratory experiments, CoFlo’s team has demonstrated injections that are up to 200 times faster.

“CoFlo is the only technology that is capable of administering viscous drugs while simultaneously optimizing the patient experience, minimizing the clinical burden, and reducing device cost,” Rufer said.

Celebrating entrepreneurship

The MIT $100K Competition started more than 30 years ago, when students, along with the late MIT Professor Ed Roberts, raised $10,000 to turn MIT’s “mens et manus” (“mind and hand”) motto into a startup challenge. Over time, with sponsor support, the event grew into the renown, highly anticipated startup competition it is today, highlighting some of the most promising new companies founded by MIT community members each year.

The Monday night event was the culmination of months of work and preparation by participating teams. The $100K program began with student pitches in December and was followed by mentorship, funding, and other support for select teams over the course of ensuing months.

This year more than 50 teams applied for the $100K’s final event. A network of external judges whittled that down to the eight finalists that made their pitches.

Other winners

In addition to the grand prize, finalists were also awarded a $50,000 second-place prize, a $5,000 third-place prize, and a $5,000 audience choice award, which was voted on during the judge’s deliberations.

The second-place prize went to Haven, an artificial intelligence-powered financial planning platform that helps families manage lifelong disability care. Haven’s pitch was delivered by Tej Mehta, a student in the MIT Sloan School of Management who explained the problem by sharing his own family’s experience managing his sister’s intellectual disability.

“As my family plans for the future, a number of questions are keeping us up at night,” Mehta told the audience. “How much money do we need to save? What public benefits is she eligible for? How do we structure our private assets so she doesn’t lose those public benefits? Finally, how do we manage the funds and compliance over time?”

Haven works by using family information and goals to build a personalized roadmap that can predict care needs and costs over more than 50 years.

“We recommend to families the exact next steps they need to take, what to apply for, and when,” Mehta explained.

The third-place prize went to Aorta Scope, which combines AI and ultrasound to provide augmented reality guidance during vascular surgery. Today, surgeons must rely on a 2-D X-ray image as they feed a large stent into patients’ body during a common surgery known as endovascular repair.

Aorta Scope has developed a platform for real-time, 3-D implant alignment. The solution combines intravascular ultrasound technology with fiber optic shape sensing. Tom Dillon built the system that combines data from those sources as part of his ongoing PhD in MIT’s Department of Mechanical Engineering.

Finally, the audience choice award went to Flood Dynamics, which provides real-time flood risk modeling to help cities, insurers, and developers adapt and protect urban communities from flooding.

Although most urban flood damages are driven by rain today, flood models don’t account for rainfall, making cities less prepared for flooding risks.

“Flooding, and especially rain-driven flooding, is the costliest natural hazard around the world today,” said Katerina Boukin SM ’20, PhD ’25, who developed the company’s technology at MIT. “The price of staying rain-blind is really steep. This is an issue that is costing the U.S. alone more than $30 billion a year.”

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