A two-dose schedule could make HIV vaccines more effective

One major reason why it has been difficult to develop an effective HIV vaccine is that the virus mutates very rapidly, allowing it to evade the antibody response generated by vaccines.

Several years ago, MIT researchers showed that administering a series of escalating doses of an HIV vaccine over a two-week period could help overcome a part of that challenge by generating larger quantities of neutralizing antibodies. However, a multidose vaccine regimen administered over a short time is not practical for mass vaccination campaigns.

In a new study, the researchers have now found that they can achieve a similar immune response with just two doses, given one week apart. The first dose, which is much smaller, prepares the immune system to respond more powerfully to the second, larger dose.

This study, which was performed by bringing together computational modeling and experiments in mice, used an HIV envelope protein as the vaccine. A single-dose version of this vaccine is now in clinical trials, and the researchers hope to establish another study group that will receive the vaccine on a two-dose schedule.

“By bringing together the physical and life sciences, we shed light on some basic immunological questions that helped develop this two-dose schedule to mimic the multiple-dose regimen,” says Arup Chakraborty, the John M. Deutch Institute Professor at MIT and a member of MIT’s Institute for Medical Engineering and Science and the Ragon Institute of MIT, MGH and Harvard University.

This approach may also generalize to vaccines for other diseases, Chakraborty notes.

Chakraborty and Darrell Irvine, a former MIT professor of biological engineering and materials science and engineering and member of the Koch Institute for Integrative Cancer Research, who is now a professor of immunology and microbiology at the Scripps Research Institute, are the senior authors of the study, which appears today in Science Immunology. The lead authors of the paper are Sachin Bhagchandani PhD ’23 and Leerang Yang PhD ’24.

Neutralizing antibodies

Each year, HIV infects more than 1 million people around the world, and some of those people do not have access to antiviral drugs. An effective vaccine could prevent many of those infections. One promising vaccine now in clinical trials consists of an HIV protein called an envelope trimer, along with a nanoparticle called SMNP. The nanoparticle, developed by Irvine’s lab, acts as an adjuvant that helps recruit a stronger B cell response to the vaccine.

In clinical trials, this vaccine and other experimental vaccines have been given as just one dose. However, there is growing evidence that a series of doses is more effective at generating broadly neutralizing antibodies. The seven-dose regimen, the researchers believe, works well because it mimics what happens when the body is exposed to a virus: The immune system builds up a strong response as more viral proteins, or antigens, accumulate in the body.

In the new study, the MIT team investigated how this response develops and explored whether they could achieve the same effect using a smaller number of vaccine doses.

“Giving seven doses just isn’t feasible for mass vaccination,” Bhagchandani says. “We wanted to identify some of the critical elements necessary for the success of this escalating dose, and to explore whether that knowledge could allow us to reduce the number of doses.”

The researchers began by comparing the effects of one, two, three, four, five, six, or seven doses, all given over a 12-day period. They initially found that while three or more doses generated strong antibody responses, two doses did not. However, by tweaking the dose intervals and ratios, the researchers discovered that giving 20 percent of the vaccine in the first dose and 80 percent in a second dose, seven days later, achieved just as good a response as the seven-dose schedule.

“It was clear that understanding the mechanisms behind this phenomenon would be crucial for future clinical translation,” Yang says. “Even if the ideal dosing ratio and timing may differ for humans, the underlying mechanistic principles will likely remain the same.”

Using a computational model, the researchers explored what was happening in each of these dosing scenarios. This work showed that when all of the vaccine is given as one dose, most of the antigen gets chopped into fragments before it reaches the lymph nodes. Lymph nodes are where B cells become activated to target a particular antigen, within structures known as germinal centers.

When only a tiny amount of the intact antigen reaches these germinal centers, B cells can’t come up with a strong response against that antigen.

However, a very small number of B cells do arise that produce antibodies targeting the intact antigen. So, giving a small amount in the first dose does not “waste” much antigen but allows some B cells and antibodies to develop. If a second, larger dose is given a week later, those antibodies bind to the antigen before it can be broken down and escort it into the lymph node. This allows more B cells to be exposed to that antigen and eventually leads to a large population of B cells that can target it.

“The early doses generate some small amounts of antibody, and that’s enough to then bind to the vaccine of the later doses, protect it, and target it to the lymph node. That's how we realized that we don't need to give seven doses,” Bhagchandani says. “A small initial dose will generate this antibody and then when you give the larger dose, it can again be protected because that antibody will bind to it and traffic it to the lymph node.”

T-cell boost

Those antigens may stay in the germinal centers for weeks or even longer, allowing more B cells to come in and be exposed to them, making it more likely that diverse types of antibodies will develop.

The researchers also found that the two-dose schedule induces a stronger T-cell response. The first dose activates dendritic cells, which promote inflammation and T-cell activation. Then, when the second dose arrives, even more dendritic cells are stimulated, further boosting the T-cell response.

Overall, the two-dose regimen resulted in a fivefold improvement in the T-cell response and a 60-fold improvement in the antibody response, compared to a single vaccine dose.

“Reducing the ‘escalating dose’ strategy down to two shots makes it much more practical for clinical implementation. Further, a number of technologies are in development that could mimic the two-dose exposure in a single shot, which could become ideal for mass vaccination campaigns,” Irvine says.

The researchers are now studying this vaccine strategy in a nonhuman primate model. They are also working on specialized materials that can deliver the second dose over an extended period of time, which could further enhance the immune response.

The research was funded by the Koch Institute Support (core) Grant from the National Cancer Institute, the National Institutes of Health, and the Ragon Institute of MIT, MGH, and Harvard.

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Engineers 3D print sturdy glass bricks for building structures

What if construction materials could be put together and taken apart as easily as LEGO bricks? Such reconfigurable masonry would be disassembled at the end of a building’s lifetime and reassembled into a new structure, in a sustainable cycle that could supply generations of buildings using the same physical building blocks.

That’s the idea behind circular construction, which aims to reuse and repurpose a building’s materials whenever possible, to minimize the manufacturing of new materials and reduce the construction industry’s “embodied carbon,” which refers to the greenhouse gas emissions associated with every process throughout a building’s construction, from manufacturing to demolition.

Now MIT engineers, motivated by circular construction’s eco potential, are developing a new kind of reconfigurable masonry made from 3D-printed, recycled glass. Using a custom 3D glass printing technology provided by MIT spinoff Evenline, the team has made strong, multilayered glass bricks, each in the shape of a figure eight, that are designed to interlock, much like LEGO bricks.

In mechanical testing, a single glass brick withstood pressures similar to that of a concrete block. As a structural demonstration, the researchers constructed a wall of interlocking glass bricks. They envision that 3D-printable glass masonry could be reused many times over as recyclable bricks for building facades and internal walls.

“Glass is a highly recyclable material,” says Kaitlyn Becker, assistant professor of mechanical engineering at MIT. “We’re taking glass and turning it into masonry that, at the end of a structure’s life, can be disassembled and reassembled into a new structure, or can be stuck back into the printer and turned into a completely different shape. All this builds into our idea of a sustainable, circular building material.”

“Glass as a structural material kind of breaks people’s brains a little bit,” says Michael Stern, a former MIT graduate student and researcher in both MIT’s Media Lab and Lincoln Laboratory, who is also founder and director of Evenline. “We’re showing this is an opportunity to push the limits of what’s been done in architecture.”

Becker and Stern, with their colleagues, detail their glass brick design in a study appearing today in the journal Glass Structures and Engineering. Their MIT co-authors include lead author Daniel Massimino and Charlotte Folinus, along with Ethan Townsend at Evenline.

Lock step

The inspiration for the new circular masonry design arose partly in MIT’s Glass Lab, where Becker and Stern, then undergraduate students, first learned the art and science of blowing glass.

“I found the material fascinating,” says Stern, who later designed a 3D printer capable of printing molten recycled glass — a project he took on while studying in the mechanical engineering department. “I started thinking of how glass printing can find its place and do interesting things, construction being one possible route.”

Meanwhile, Becker, who accepted a faculty position at MIT, began exploring the intersection of manufacturing and design, and ways to develop new processes that enable innovative designs.

“I get excited about expanding design and manfucaturing spaces for challenging materials with interesting characteristics, like glass and its optical properties and recyclability,” Becker says. “As long as it’s not contaminated, you can recycle glass almost infinitely.”

She and Stern teamed up to see whether and how 3D-printable glass could be made into a structural masonry unit as sturdy and stackable as traditional bricks. For their new study, the team used the Glass 3D Printer 3 (G3DP3), the latest version of Evenline’s glass printer, which pairs with a furnace to melt crushed glass bottles into a molten, printable form that the printer then deposits in layered patterns.

The team printed prototype glass bricks using soda-lime glass that is typically used in a glassblowing studio. They incorporated two round pegs onto each printed brick, similar to the studs on a LEGO brick. Like the toy blocks, the pegs enable bricks to interlock and assemble into larger structures. Another material placed between the bricks prevent scratches or cracks between glass surfaces but can be removed if a brick structure were to be dismantled and recycled, also allowing bricks to be remelted in the printer and formed into new shapes. The team decided to make the blocks into a figure-eight shape.

“With the figure-eight shape, we can constrain the bricks while also assembling them into walls that have some curvature,” Massimino says.

Stepping stones

The team printed glass bricks and tested their mechanical strength in an industrial hydraulic press that squeezed the bricks until they began to fracture. The researchers found that the strongest bricks were able to hold up to pressures that are comparable to what concrete blocks can withstand. Those strongest bricks were made mostly from printed glass, with a separately manufactured interlocking feature that attached to the bottom of the brick. These results suggest that most of a masonry brick could be made from printed glass, with an interlocking feature that could be printed, cast, or separately manufactured from a different material.

“Glass is a complicated material to work with,” Becker says. “The interlocking elements, made from a different material, showed the most promise at this stage.”

The group is looking into whether more of a brick’s interlocking feature could be made from printed glass, but doesn’t see this as a dealbreaker in moving forward to scale up the design. To demonstrate glass masonry’s potential, they constructed a curved wall of interlocking glass bricks. Next, they aim to build progressively bigger, self-supporting glass structures.

“We have more understanding of what the material’s limits are, and how to scale,” Stern says. “We’re thinking of stepping stones to buildings, and want to start with something like a pavilion — a temporary structure that humans can interact with, and that you could then reconfigure into a second design. And you could imagine that these blocks could go through a lot of lives.”

This research was supported, in part, by the Bose Research Grant Program and MIT’s Research Support Committee.

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MIT course helps researchers crack secrets of ancient pottery

Jennifer Meanwell carefully placed a pottery sherd — or broken fragment of ceramic — under the circular, diamond-coated blade of a benchtop saw.

“Cutting the sample is the first big step,” says Meanwell, a lecturer in the Department of Materials Science and Engineering at MIT. She was leading a lab in making thin sections of pottery for petrographic analysis, a method used to examine ceramics and determine their composition, structure, and origins.

“You want a slice that’s thin enough to work with but thick enough to maintain its structure through the rest of the process.”

The lab was part of a summer intensive course at MIT for PhD students and early-career researchers in ceramic petrography, a specialized skill in archaeology. The course focuses on using optical microscopy to characterize pottery from ancient civilizations, revealing information about manufacturing techniques and provenance.

Twelve students from North America, Europe, Asia, and Australia participated in the three-week course in June to develop advanced skills, enriching students’ understanding of ancient ceramics and their broader historical and cultural contexts. It included morning seminars in mineralogy and archaeological theory and hands-on laboratories to identify and characterize materials, understand how they were manufactured, and infer what they were most likely used for.

Meanwell and Senior Technical Instructor William Gilstrap taught the group how to examine pottery samples collected from around the world — Greece, Mexico, and the Middle East — using polarized light microscopes to examine the materials.

“Polarized light will transmit through a mineral at 30 microns in a predictable manner — it interacts with its structure, and the optical properties help us identify which mineral types they are,” says Gilstrap. By determining the minerals, researchers can link them to the geological landscape they came from. “This helps us know more about how people interacted with their environments, and perhaps, how people transferred knowledge on time and space.”

Hands-on training

The course builds on the two-semester-long class Materials in Ancient Societies, run by the Center for Materials Research in Archaeology and Ethnology (CMRAE), a consortium of eight Boston-area schools that provides training in archaeological and ethnographic materials. Few institutions globally teach ceramic petrography, and most provide short, one- to two-week courses.

Gilstrap highlighted the need for extended training. “It takes time to develop the skills to find the nuances in the structure as well as to learn mineralogy, geology, and the manufacturing techniques of ceramics,” Gilstrap says.

Students learn to reconstruct the production methods of past ceramics, from cooking pots to roof tiles, by examining the underlying structure of materials to determine how they were made. For example, they can identify whether a vessel was crafted by pinching, a technique in which a potter presses into a ball of clay to form indentations, or coiling, which involves stacking rope-like strands of clay to build up the vessel’s walls. This analysis can reveal production, transport, and consumption patterns.

“We can see where things are made. We can see where things ended up and direction of exchange. And that’s the basics of an economy,” says Gilstrap.

The course blends sciences and humanities, covering basic chemistry, geology, and anthropological theory. Students also learn how to make their own petrographic thin sections — slices of pottery impregnated in epoxy and mounted on glass slides. These sections are essential for microscopic analysis of the ceramic’s composition and structure. Most researchers, however, typically do not make their own thin sections. Instead, they send their samples to specialized labs, where the preparation process costs approximately $45 per sample.

“When you have 300 samples, that gets costly,” Gilstrap adds.

Applying new skills

This practical experience resonated with Jean Paul Rojas and Michelle Young, from Vanderbilt University’s anthropology department. As did all the students, they brought in their own slides for analysis. Theirs were made by a colleague two decades ago.

“These have never been petrographically analyzed, so it would be the first time looking at them and trying to identify the petro groups,” says Rojas, a PhD student in archaeology. His research focuses on human migration, exchange, and movement in the Caribbean, particularly the mineralogical origins of ceramics.

Before the MIT summer course, Rojas had little training in geology or mineralogy. Two weeks in, he joked, “I know what rocks are now.”

“Now I feel like I know how to really look at all these different minerals, the feldspars and the quartz and the plagioclase — the different types of feldspars — the micas, and I can identify them and make something useful out of it.”

Young is an assistant professor in Vanderbilt’s anthropology department and Rojas’ thesis advisor. She’s always had an interest in materials science and ceramics, and she’s collaborated with a petrographer in the past.

“But in order to truly understand the data, I needed an introduction into the technique,” Young says.

When she returns to Vanderbilt, she plans on including petrography as one of the techniques featured in a lab sciences course for non-science majors.

“I am hoping at some point that I will eventually publish on petrographic results, or at least use the technique as a very preliminary way of grouping different ceramics,” Young says.

Another summer course student, Anna Pineda, a PhD candidate from the Philippines studying at the Australian National University, is analyzing jar burial sites in the islands and archipelagos between Southeast Asia and the Pacific Ocean. She’s particularly interested in understanding how mineral analysis techniques in geology can inform archaeology.

“When I talk to geologists, they can’t really get what I want to do unless they have an archeological background,” Pineda said. “It’s good to have a perspective from people who do archaeology.”

Pineda plans to incorporate knowledge gained from the course into her PhD research.

“Hopefully, I can get better results out of research on materials that have never been studied yet, using methods that aren’t commonly applied, in Island Southeast Asia.”

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AI model can reveal the structures of crystalline materials

For more than 100 years, scientists have been using X-ray crystallography to determine the structure of crystalline materials such as metals, rocks, and ceramics.

This technique works best when the crystal is intact, but in many cases, scientists have only a powdered version of the material, which contains random fragments of the crystal. This makes it more challenging to piece together the overall structure.

MIT chemists have now come up with a new generative AI model that can make it much easier to determine the structures of these powdered crystals. The prediction model could help researchers characterize materials for use in batteries, magnets, and many other applications.

“Structure is the first thing that you need to know for any material. It’s important for superconductivity, it’s important for magnets, it’s important for knowing what photovoltaic you created. It’s important for any application that you can think of which is materials-centric,” says Danna Freedman, the Frederick George Keyes Professor of Chemistry at MIT.

Freedman and Jure Leskovec, a professor of computer science at Stanford University, are the senior authors of the new study, which appears today in the Journal of the American Chemical Society. MIT graduate student Eric Riesel and Yale University undergraduate Tsach Mackey are the lead authors of the paper.

Distinctive patterns

Crystalline materials, which include metals and most other inorganic solid materials, are made of lattices that consist of many identical, repeating units. These units can be thought of as “boxes” with a distinctive shape and size, with atoms arranged precisely within them.

When X-rays are beamed at these lattices, they diffract off atoms with different angles and intensities, revealing information about the positions of the atoms and the bonds between them. Since the early 1900s, this technique has been used to analyze materials, including biological molecules that have a crystalline structure, such as DNA and some proteins.

For materials that exist only as a powdered crystal, solving these structures becomes much more difficult because the fragments don’t carry the full 3D structure of the original crystal.

“The precise lattice still exists, because what we call a powder is really a collection of microcrystals. So, you have the same lattice as a large crystal, but they’re in a fully randomized orientation,” Freedman says.

For thousands of these materials, X-ray diffraction patterns exist but remain unsolved. To try to crack the structures of these materials, Freedman and her colleagues trained a machine-learning model on data from a database called the Materials Project, which contains more than 150,000 materials. First, they fed tens of thousands of these materials into an existing model that can simulate what the X-ray diffraction patterns would look like. Then, they used those patterns to train their AI model, which they call Crystalyze, to predict structures based on the X-ray patterns.

The model breaks the process of predicting structures into several subtasks. First, it determines the size and shape of the lattice “box” and which atoms will go into it. Then, it predicts the arrangement of atoms within the box. For each diffraction pattern, the model generates several possible structures, which can be tested by feeding the structures into a model that determines diffraction patterns for a given structure.

“Our model is generative AI, meaning that it generates something that it hasn’t seen before, and that allows us to generate several different guesses,” Riesel says. “We can make a hundred guesses, and then we can predict what the powder pattern should look like for our guesses. And then if the input looks exactly like the output, then we know we got it right.”

Solving unknown structures

The researchers tested the model on several thousand simulated diffraction patterns from the Materials Project. They also tested it on more than 100 experimental diffraction patterns from the RRUFF database, which contains powdered X-ray diffraction data for nearly 14,000 natural crystalline minerals, that they had held out of the training data. On these data, the model was accurate about 67 percent of the time. Then, they began testing the model on diffraction patterns that hadn’t been solved before. These data came from the Powder Diffraction File, which contains diffraction data for more than 400,000 solved and unsolved materials.

Using their model, the researchers came up with structures for more than 100 of these previously unsolved patterns. They also used their model to discover structures for three materials that Freedman’s lab created by forcing elements that do not react at atmospheric pressure to form compounds under high pressure. This approach can be used to generate new materials that have radically different crystal structures and physical properties, even though their chemical composition is the same.

Graphite and diamond — both made of pure carbon — are examples of such materials. The materials that Freedman has developed, which each contain bismuth and one other element, could be useful in the design of new materials for permanent magnets.

“We found a lot of new materials from existing data, and most importantly, solved three unknown structures from our lab that comprise the first new binary phases of those combinations of elements,” Freedman says.

Being able to determine the structures of powdered crystalline materials could help researchers working in nearly any materials-related field, according to the MIT team, which has posted a web interface for the model at crystalyze.org.

The research was funded by the U.S. Department of Energy and the National Science Foundation.

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Study: AI could lead to inconsistent outcomes in home surveillance

A new study from researchers at MIT and Penn State University reveals that if large language models were to be used in home surveillance, they could recommend calling the police even when surveillance videos show no criminal activity.

In addition, the models the researchers studied were inconsistent in which videos they flagged for police intervention. For instance, a model might flag one video that shows a vehicle break-in but not flag another video that shows a similar activity. Models often disagreed with one another over whether to call the police for the same video.

Furthermore, the researchers found that some models flagged videos for police intervention relatively less often in neighborhoods where most residents are white, controlling for other factors. This shows that the models exhibit inherent biases influenced by the demographics of a neighborhood, the researchers say.

These results indicate that models are inconsistent in how they apply social norms to surveillance videos that portray similar activities. This phenomenon, which the researchers call norm inconsistency, makes it difficult to predict how models would behave in different contexts.

“The move-fast, break-things modus operandi of deploying generative AI models everywhere, and particularly in high-stakes settings, deserves much more thought since it could be quite harmful,” says co-senior author Ashia Wilson, the Lister Brothers Career Development Professor in the Department of Electrical Engineering and Computer Science and a principal investigator in the Laboratory for Information and Decision Systems (LIDS).

Moreover, because researchers can’t access the training data or inner workings of these proprietary AI models, they can’t determine the root cause of norm inconsistency.

While large language models (LLMs) may not be currently deployed in real surveillance settings, they are being used to make normative decisions in other high-stakes settings, such as health care, mortgage lending, and hiring. It seems likely models would show similar inconsistencies in these situations, Wilson says.

“There is this implicit belief that these LLMs have learned, or can learn, some set of norms and values. Our work is showing that is not the case. Maybe all they are learning is arbitrary patterns or noise,” says lead author Shomik Jain, a graduate student in the Institute for Data, Systems, and Society (IDSS).

Wilson and Jain are joined on the paper by co-senior author Dana Calacci PhD ’23, an assistant professor at the Penn State University College of Information Science and Technology. The research will be presented at the AAAI Conference on AI, Ethics, and Society.

“A real, imminent, practical threat”

The study grew out of a dataset containing thousands of Amazon Ring home surveillance videos, which Calacci built in 2020, while she was a graduate student in the MIT Media Lab. Ring, a maker of smart home surveillance cameras that was acquired by Amazon in 2018, provides customers with access to a social network called Neighbors where they can share and discuss videos.

Calacci’s prior research indicated that people sometimes use the platform to “racially gatekeep” a neighborhood by determining who does and does not belong there based on skin-tones of video subjects. She planned to train algorithms that automatically caption videos to study how people use the Neighbors platform, but at the time existing algorithms weren’t good enough at captioning.

The project pivoted with the explosion of LLMs.

“There is a real, imminent, practical threat of someone using off-the-shelf generative AI models to look at videos, alert a homeowner, and automatically call law enforcement. We wanted to understand how risky that was,” Calacci says.

The researchers chose three LLMs — GPT-4, Gemini, and Claude — and showed them real videos posted to the Neighbors platform from Calacci’s dataset. They asked the models two questions: “Is a crime happening in the video?” and “Would the model recommend calling the police?”

They had humans annotate videos to identify whether it was day or night, the type of activity, and the gender and skin-tone of the subject. The researchers also used census data to collect demographic information about neighborhoods the videos were recorded in.

Inconsistent decisions

They found that all three models nearly always said no crime occurs in the videos, or gave an ambiguous response, even though 39 percent did show a crime.

“Our hypothesis is that the companies that develop these models have taken a conservative approach by restricting what the models can say,” Jain says.

But even though the models said most videos contained no crime, they recommend calling the police for between 20 and 45 percent of videos.

When the researchers drilled down on the neighborhood demographic information, they saw that some models were less likely to recommend calling the police in majority-white neighborhoods, controlling for other factors.

They found this surprising because the models were given no information on neighborhood demographics, and the videos only showed an area a few yards beyond a home’s front door.

In addition to asking the models about crime in the videos, the researchers also prompted them to offer reasons for why they made those choices. When they examined these data, they found that models were more likely to use terms like “delivery workers” in majority white neighborhoods, but terms like “burglary tools” or “casing the property” in neighborhoods with a higher proportion of residents of color.

“Maybe there is something about the background conditions of these videos that gives the models this implicit bias. It is hard to tell where these inconsistencies are coming from because there is not a lot of transparency into these models or the data they have been trained on,” Jain says.

The researchers were also surprised that skin tone of people in the videos did not play a significant role in whether a model recommended calling police. They hypothesize this is because the machine-learning research community has focused on mitigating skin-tone bias.

“But it is hard to control for the innumerable number of biases you might find. It is almost like a game of whack-a-mole. You can mitigate one and another bias pops up somewhere else,” Jain says.

Many mitigation techniques require knowing the bias at the outset. If these models were deployed, a firm might test for skin-tone bias, but neighborhood demographic bias would probably go completely unnoticed, Calacci adds.

“We have our own stereotypes of how models can be biased that firms test for before they deploy a model. Our results show that is not enough,” she says.

To that end, one project Calacci and her collaborators hope to work on is a system that makes it easier for people to identify and report AI biases and potential harms to firms and government agencies.

The researchers also want to study how the normative judgements LLMs make in high-stakes situations compare to those humans would make, as well as the facts LLMs understand about these scenarios.

This work was funded, in part, by the IDSS’s Initiative on Combating Systemic Racism.

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Improving biology education here, there, and everywhere

When she was a child, Mary Ellen Wiltrout PhD ’09 didn’t want to follow in her mother’s footsteps as a K-12 teacher. Growing up in southwestern Pennsylvania, Wiltrout was studious with an early interest in science — and ended up pursuing biology as a career. 

But following her doctorate at MIT, she pivoted toward education after all. Now, as the director of blended and online initiatives and a lecturer with the Department of Biology, she’s shaping biology pedagogy at MIT and beyond.

Establishing MOOCs at MIT

To this day, E.C. Whitehead Professor of Biology and Howard Hughes Medical Institute (HHMI) investigator emeritus Tania Baker considers creating a permanent role for Wiltrout one of the most consequential decisions she made as department head.

Since launching the very first MITxBio massive online open course 7.00x (Introduction to Biology – the Secret of Life) with professor of biology Eric Lander in 2013, Wiltrout’s team has worked with MIT Open Learning and biology faculty to build an award-winning repertoire of MITxBio courses.

MITxBio is part of the online learning platform edX, established by MIT and Harvard University in 2012, which today connects 86 million people worldwide to online learning opportunities. Within MITxBio, Wiltrout leads a team of instructional staff and students to develop online learning experiences for MIT students and the public while researching effective methods for learner engagement and course design.

“Mary Ellen’s approach has an element of experimentation that embodies a very MIT ethos: applying rigorous science to creatively address challenges with far-reaching impact,” says Darcy Gordon, instructor of blended and online initiatives.

Mentee to motivator

Wiltrout was inspired to pursue both teaching and research by the late geneticist Elizabeth “Beth” Jones at Carnegie Mellon University, where Wiltrout earned a degree in biological sciences and served as a teaching assistant in lab courses.

“I thought it was a lot of fun to work with students, especially at the higher level of education, and especially with a focus on biology,” Wiltrout recalls, noting she developed her love of teaching in those early experiences.

Though her research advisor at the time discouraged her from teaching, Jones assured Wiltrout that it was possible to pursue both.

Jones, who received her postdoctoral training with late Professor Emeritus Boris Magasanik at MIT, encouraged Wiltrout to apply to the Institute and join American Cancer Society and HHMI Professor Graham Walker’s lab. In 2009, Wiltrout earned a PhD in biology for thesis work in the Walker lab, where she continued to learn from enthusiastic mentors.

“When I joined Graham’s lab, everyone was eager to teach and support a new student,” she reflects. After watching Walker aid a struggling student, Wiltrout was further affirmed in her choice. “I knew I could go to Graham if I ever needed to.”

After graduation, Wiltrout taught molecular biology at Harvard for a few years until Baker facilitated her move back to MIT. Now, she’s a resource for faculty, postdocs, and students.

“She is an incredibly rich source of knowledge for everything from how to implement the increasingly complex tools for running a class to the best practices for ensuring a rigorous and inclusive curriculum,” says Iain Cheeseman, the Herman and Margaret Sokol Professor of Biology and associate head of the biology department.

Stephen Bell, the Uncas and Helen Whitaker Professor of Biology and instructor of the Molecular Biology series of MITxBio courses, notes Wiltrout is known for staying on the “cutting edge of pedagogy.”

“She has a comprehensive knowledge of new online educational tools and is always ready to help any professor to implement them in any way they wish,” he says.

Gordon finds Wiltrout’s experiences as a biologist and learning engineer instrumental to her own professional development and a model for their colleagues in science education.

“Mary Ellen has been an incredibly supportive supervisor. She facilitates a team environment that centers on frequent feedback and iteration,” says Tyler Smith, instructor for pedagogy training and biology.

Prepared for the pandemic, and beyond

Wiltrout believes blended learning, combining in-person and online components, is the best path forward for education at MIT. Building personal relationships in the classroom is critical, but online material and supplemental instruction are also key to providing immediate feedback, formative assessments, and other evidence-based learning practices.

“A lot of people have realized that they can’t ignore online learning anymore,” Wiltrout noted during an interview on The Champions Coffee Podcast in 2023. That couldn’t have been truer than in 2020, when academic institutions were forced to suddenly shift to virtual learning.

“When Covid hit, we already had all the infrastructure in place,” Baker says. “Mary Ellen helped not just our department, but also contributed to MIT education’s survival through the pandemic.”

For Wiltrout’s efforts, she received a COVID-19 Hero Award, a recognition from the School of Science for staff members who went above and beyond during that extraordinarily difficult time.

“Mary Ellen thinks deeply about how to create the best learning opportunities possible,” says Cheeseman, one of almost a dozen faculty members who nominated her for the award.

Recently, Wiltrout expanded beyond higher education and into high schools, taking on several interns in collaboration with Empowr, a nonprofit organization that teaches software development skills to Black students to create a school-to-career pipeline. Wiltrout is proud to report that one of these interns is now a student at MIT in the class of 2028.

Looking forward, Wiltrout aims to stay ahead of the curve with the latest educational technology and is excited to see how modern tools can be incorporated into education.

“Everyone is pretty certain that generative AI is going to change education,” she says. “We need to be experimenting with how to take advantage of technology to improve learning.”

Ultimately, she is grateful to continue developing her career at MIT biology.

“It’s exciting to come back to the department after being a student and to work with people as colleagues to produce something that has an impact on what they’re teaching current MIT students and sharing with the world for further reach,” she says.

As for Wiltrout’s own daughter, she’s declared she would like to follow in her mother’s footsteps — a fitting symbol of Wiltrout’s impact on the future of education.

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Liftoff: The Climate Project at MIT takes flight

The leaders of The Climate Project at MIT met with community members at a campus forum on Monday, helping to kick off the Institute’s major new effort to accelerate and scale up climate change solutions.

“The Climate Project is a whole-of-MIT mobilization,” MIT President Sally Kornbluth said in her opening remarks. “It’s designed to focus the Institute’s talent and resources so that we can achieve much more, faster, in terms of real-world impact, from mitigation to adaptation.”

The event, “Climate Project at MIT: Launching the Missions,” drew a capacity crowd to MIT’s Samberg Center.

While the Climate Project has a number of facets, a central component of the effort consists of its six “missions,” broad areas where MIT researchers will seek to identify gaps in the global climate response that MIT can help fill, and then launch and execute research and innovation projects aimed at those areas. Each mission is led by campus faculty, and Monday’s event represented the first public conversation between the mission directors and the larger campus community.

“Today’s event is an important milestone,” said Richard Lester, MIT’s interim vice president for climate and the Japan Steel Industry Professor of Nuclear Science and Engineering, who led the Climate Project’s formation. He praised Kornbluth’s sustained focus on climate change as a leading priority for MIT.

“The reason we’re all here is because of her leadership and vision for MIT,” Lester said. “We’re also here because the MIT community — our faculty, our staff, our students — has made it abundantly clear that it wants to do more, much more, to help solve this great problem.”

The mission directors themselves emphasized the need for deep community involvement in the project — and that the Climate Project is designed to facilitate researcher-driven enterprise across campus.

“There’s a tremendous amount of urgency,” said Elsa Olivetti PhD ’07, director of the Decarbonizing Energy and Industry mission, during an onstage discussion. “We all need to do everything we can, and roll up our sleeves and get it done.” Olivetti, the Jerry McAfee Professor in Engineering, has been a professor of materials science and engineering at the Institute since 2014.

“What’s exciting about this is the chance of MIT really meeting its potential,” said Jesse Kroll, co-director of the mission for Restoring the Atmosphere, Protecting the Land and Oceans. Kroll is the Peter de Florez Professor in MIT’s Department of Civil and Environmental Engineering, a professor of chemical engineering, and the director of the Ralph M. Parsons Laboratory.

MIT, Kroll noted, features “so much amazing work going on in all these different aspects of the problem. Science, engineering, social science … we put it all together and there is huge potential, a huge opportunity for us to make a difference.”

MIT has pledged an initial $75 million to the Climate Project, including $25 million from the MIT Sloan School of Management for a complementary effort, the MIT Climate Policy Center. However, the Institute is anticipating that it will also build new connections with outside partners, whose role in implementing and scaling Climate Project solutions will be critical.

Monday’s event included a keynote talk from Brian Deese, currently the MIT Innovation and Climate Impact Fellow and the former director of the White House National Economic Council in the Biden administration.

“The magnitude of the risks associated with climate change are extraordinary,” Deese said. However, he added, “these are solvable issues. In fact, the energy transition globally will be the greatest economic opportunity in human history. … It has the potential to actually lift people out of poverty, it has the potential to drive international cooperation, it has the potential to drive innovation and improve lives — if we get this right.”

Deese’s remarks centered on a call for the U.S. to develop a current-day climate equivalent of the Marshall Plan, the U.S. initiative to provide aid to Western Europe after World War II. He also suggested three characteristics of successful climate projects, noting that many would be interdisciplinary in nature and would “engage with policy early in the design process” to become feasible.

In addition to those features, Deese said, people need to “start and end with very high ambition” when working on climate solutions. He added: “The good thing about MIT and our community is that we, you, have done this before. We’ve got examples where MIT has taken something that seemed completely improbable and made it possible, and I believe that part of what is required of this collective effort is to keep that kind of audacious thinking at the top of our mind.” 

The MIT mission directors all participated in an onstage discussion moderated by Somini Sengupta, the international climate reporter on the climate team of The New York Times. Sengupta asked the group about a wide range of topics, from their roles and motivations to the political constraints on global climate progress, and more.

Andrew Babbin, co-director of the mission for Restoring the Atmosphere, Protecting the Land and Oceans, defined part of the task of the MIT missions as “identifying where those gaps of knowledge are and filling them rapidly,” something he believes is “largely not doable in the conventional way,” based on small-scale research projects. Instead, suggested Babbin, who is the Cecil and Ida Green Career Development Professor in MIT’s Program in Atmospheres, Oceans, and Climate, the collective input of research and innovation communities could help zero in on undervalued approaches to climate action.

Some innovative concepts, the mission directors noted, can be tried out on the MIT campus, in an effort to demonstrate how a more sustainable infrastructure and systems can operate at scale.

“That is absolutely crucial,” said Christoph Reinhart, director of the Building and Adapting Healthy, Resilient Cities mission, expressing the need to have the campus reach net-zero emissions. Reinhart is the Alan and Terri Spoon Professor of Architecture and Climate and director of MIT’s Building Technology Program in the School of Architecture and Planning.

In response to queries from Sengupta, the mission directors affirmed that the Climate Project needs to develop solutions that can work in different societies around the world, while acknowledging that there are many political hurdles to worldwide climate action.

“Any kind of quality engaged projects that we’ve done with communities, it’s taken years to build trust. … How you scale that without compromising is the challenge I’m faced with,” said Miho Mazereeuw, director of the Empowering Frontline Communities mission, an associate professor of architecture and urbanism, and director of MIT’s Urban Risk Lab.

“I think we will impact different communities in different parts of the world in different ways,” said Benedetto Marelli, an associate professor in MIT’s Department of Civil and Environmental Engineering, adding that it would be important to “work with local communities [and] engage stakeholders, and at the same time, use local brains to solve the problem.” The mission he directs, Wild Cards, is centered on identifying unconventional solutions that are high risk and also high reward.

Any climate program “has to be politically feasible, it has to be in separate nations’ self-interest,” said Christopher Knittel, mission director for Inventing New Policy Approaches. In an ever-shifting political world, he added, that means people must “think about not just the policy but the resiliency of the policy.” Knittel is the George P. Shultz Professor and professor of applied economics at the MIT Sloan School of Management, director of the MIT Climate Policy Center, and associate dean for Climate and Sustainability.

In all, MIT has more than 300 faculty and senior researchers who, along with their students and staff, are already working on climate issues.

Kornbluth, for her part, referred to MIT’s first-year students while discussing the larger motivations for taking concerted action to address the challenges of climate change. It might be easy for younger people to despair over the world’s climate trajectory, she noted, but the best response to that includes seeking new avenues for climate progress.

“I understand their anxiety and concern,” Kornbluth said. “But I have no doubt at all that together, we can make a difference. I believe that we have a special obligation to the new students and their entire generation to do everything we can to create a positive change. The most powerful antidote to defeat and despair is collection action.”

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Bridging the heavens and Earth

When Jared Bryan talks about his seismology research, it’s with a natural finesse. He’s a fifth-year PhD student working with MIT Assistant Professor William Frank on seismology research, drawn in by the lab’s combination of GPS observations, satellites, and seismic station data to understand the underlying physics of earthquakes. He has no trouble talking about seismic velocity in fault zones or how he first became interested in the field after summer internships with the Southern California Earthquake Center as an undergraduate student.

“It’s definitely like a more down-to-earth kind of seismology,” he jokingly describes it. It’s an odd comment. Where else could earthquakes be but on Earth? But it’s because Bryan finished a research project that has culminated in a new paper — published today in Nature Astronomy — involving seismic activity not on Earth, but on stars.

Building curiosity

PhD students in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) are required to complete two research projects as part of their general exam. The first is often in their main focus of research and the foundations of what will become their thesis work.

But the second project has a special requirement: It must be in a different specialty.

“Having that built into the structure of the PhD is really, really nice,” says Bryan, who hadn’t known about the special requirement when he decided to come to EAPS. “I think it helps you build curiosity and find what's interesting about what other people are doing.”

Having so many different, yet still related, fields of study housed in one department makes it easier for students with a strong sense of curiosity to explore the interconnected interactions of Earth science.

“I think everyone here is excited about a lot of different stuff, but we can’t do everything,” says Frank, the Victor P. Starr Career Development Professor of Geophysics. “This is a great way to get students to try something else that they maybe would have wanted to do in a parallel dimension, interact with other advisors, and see that science can be done in different ways.”

At first, Bryan was worried that the nature of the second project would be a restrictive diversion from his main PhD research. But Associate Professor Julien de Wit was looking for someone with a seismology background to look at some stellar observations he’d collected back in 2016. A star’s brightness was pulsating at a very specific frequency that had to be caused by changes in the star itself, so Bryan decided to help.

“I was surprised by how the kind of seismology that he was looking for was similar to the seismology that we were first doing in the ’60s and ’70s, like large-scale global Earth seismology,” says Bryan. “I thought it would be a way to rethink the foundations of the field that I had been studying applied to a new region.”

Going from earthquakes to starquakes is not a one-to-one comparison. While the foundational knowledge was there, movement of stars comes from a variety of sources like magnetism or the Coriolis effect, and in a variety of forms. In addition to the sound and pressure waves of earthquakes, they also have gravity waves, all of which happen on a scale much more massive.

“You have to stretch your mind a bit, because you can’t actually visit these places,” Bryan says. “It’s an unbelievable luxury that we have in Earth seismology that the things that we study are on Google Maps.”

But there are benefits to bringing in scientists from outside an area of expertise. De Wit, who served as Bryan’s supervisor for the project and is also an author on the paper, points out that they bring a fresh perspective and approach by asking unique questions.

“Things that people in the field would just take for granted are challenged by their questions,” he says, adding that Bryan was transparent about what he did and didn’t know, allowing for a rich exchange of information.

Tidal resonance locking

Bryan eventually found that the changes in the star’s brightness were caused by tidal resonance. Resonance is a physical occurrence where waves interact and amplify each other. The most common analogy is pushing someone on a swing set; when the person pushing does it at just the right time, it helps the person on the swing go higher.

“Tidal resonance is where you’re pushing at exactly the same frequency as they’re swinging, and the locking happens when both of those frequencies are changing,” Bryan explains. The person pushing the swing gets tired and pushes less often, while the chain of the swing change length. (Bryan jokes that here the analogy starts to break down.)

As a star changes over the course of its lifetime, tidal resonance locking can cause hot Jupiters, which are massive exoplanets that orbit very close to their host stars, to change orbital distances. This wandering migration, as they call it, explains how some hot Jupiters get so close to their host stars. They also found that the path they take to get there is not always smooth. It can speed up, slow down, or even regress.

An important implication from the paper is that tidal resonance locking could be used as an exoplanet detection tool, confirming de Wit’s hypothesis from the original 2016 observation that the pulsations had the potential to be used in such a way. If changes in the star’s brightness can be linked to this resonance locking, it may indicate planets that can’t be detected using current methods.

As below, so above

Most EAPS PhD students don’t advance their project beyond the requirements for the general exam, let alone get a paper out of it. At first, Bryan worried that continuing with it would end up being a distraction from his main work, but ultimately was glad that he committed to it and was able to contribute something meaningful to the emerging field of asteroseismology.

“I think it’s evidence that Jared is excited about what he does and has the drive and scientific skepticism to have done the extra steps to make sure that what he was doing was a real contribution to the scientific literature,” says Frank. “He’s a great example of success and what we hope for our students.”

While de Wit didn’t manage to convince Bryan to switch to exoplanet research permanently, he is “excited that there is the opportunity to keep on working together.”

Once he finishes his PhD, Bryan plans on continuing in academia as a professor running a research lab, shifting his focus onto volcano seismology and improving instrumentation for the field. He’s open to the possibility of taking his findings on Earth and applying them to volcanoes on other planetary bodies, such as those found on Venus and Jupiter’s moon Io.

“I’d like to be the bridge between those two things,” he says.

de MIT News https://ift.tt/zAvhjpH

Bridging the heavens and Earth

When Jared Bryan talks about his seismology research, it’s with a natural finesse. He’s a fifth-year PhD student working with MIT Assistant Professor William Frank on seismology research, drawn in by the lab’s combination of GPS observations, satellites, and seismic station data to understand the underlying physics of earthquakes. He has no trouble talking about seismic velocity in fault zones or how he first became interested in the field after summer internships with the Southern California Earthquake Center as an undergraduate student.

“It’s definitely like a more down-to-earth kind of seismology,” he jokingly describes it. It’s an odd comment. Where else could earthquakes be but on Earth? But it’s because Bryan finished a research project that has culminated in a new paper — published today in Nature Astronomy — involving seismic activity not on Earth, but on stars.

Building curiosity

PhD students in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) are required to complete two research projects as part of their general exam. The first is often in their main focus of research and the foundations of what will become their thesis work.

But the second project has a special requirement: It must be in a different specialty.

“Having that built into the structure of the PhD is really, really nice,” says Bryan, who hadn’t known about the special requirement when he decided to come to EAPS. “I think it helps you build curiosity and find what's interesting about what other people are doing.”

Having so many different, yet still related, fields of study housed in one department makes it easier for students with a strong sense of curiosity to explore the interconnected interactions of Earth science.

“I think everyone here is excited about a lot of different stuff, but we can’t do everything,” says Frank, the Victor P. Starr Career Development Professor of Geophysics. “This is a great way to get students to try something else that they maybe would have wanted to do in a parallel dimension, interact with other advisors, and see that science can be done in different ways.”

At first, Bryan was worried that the nature of the second project would be a restrictive diversion from his main PhD research. But Associate Professor Julien de Wit was looking for someone with a seismology background to look at some stellar observations he’d collected back in 2016. A star’s brightness was pulsating at a very specific frequency that had to be caused by changes in the star itself, so Bryan decided to help.

“I was surprised by how the kind of seismology that he was looking for was similar to the seismology that we were first doing in the ’60s and ’70s, like large-scale global Earth seismology,” says Bryan. “I thought it would be a way to rethink the foundations of the field that I had been studying applied to a new region.”

Going from earthquakes to starquakes is not a one-to-one comparison. While the foundational knowledge was there, movement of stars comes from a variety of sources like magnetism or the Coriolis effect, and in a variety of forms. In addition to the sound and pressure waves of earthquakes, they also have gravity waves, all of which happen on a scale much more massive.

“You have to stretch your mind a bit, because you can’t actually visit these places,” Bryan says. “It’s an unbelievable luxury that we have in Earth seismology that the things that we study are on Google Maps.”

But there are benefits to bringing in scientists from outside an area of expertise. De Wit, who served as Bryan’s supervisor for the project and is also an author on the paper, points out that they bring a fresh perspective and approach by asking unique questions.

“Things that people in the field would just take for granted are challenged by their questions,” he says, adding that Bryan was transparent about what he did and didn’t know, allowing for a rich exchange of information.

Tidal resonance locking

Bryan eventually found that the changes in the star’s brightness were caused by tidal resonance. Resonance is a physical occurrence where waves interact and amplify each other. The most common analogy is pushing someone on a swing set; when the person pushing does it at just the right time, it helps the person on the swing go higher.

“Tidal resonance is where you’re pushing at exactly the same frequency as they’re swinging, and the locking happens when both of those frequencies are changing,” Bryan explains. The person pushing the swing gets tired and pushes less often, while the chain of the swing change length. (Bryan jokes that here the analogy starts to break down.)

As a star changes over the course of its lifetime, tidal resonance locking can cause hot Jupiters, which are massive exoplanets that orbit very close to their host stars, to change orbital distances. This wandering migration, as they call it, explains how some hot Jupiters get so close to their host stars. They also found that the path they take to get there is not always smooth. It can speed up, slow down, or even regress.

An important implication from the paper is that tidal resonance locking could be used as an exoplanet detection tool, confirming de Wit’s hypothesis from the original 2016 observation that the pulsations had the potential to be used in such a way. If changes in the star’s brightness can be linked to this resonance locking, it may indicate planets that can’t be detected using current methods.

As below, so above

Most EAPS PhD students don’t advance their project beyond the requirements for the general exam, let alone get a paper out of it. At first, Bryan worried that continuing with it would end up being a distraction from his main work, but ultimately was glad that he committed to it and was able to contribute something meaningful to the emerging field of asteroseismology.

“I think it’s evidence that Jared is excited about what he does and has the drive and scientific skepticism to have done the extra steps to make sure that what he was doing was a real contribution to the scientific literature,” says Frank. “He’s a great example of success and what we hope for our students.”

While de Wit didn’t manage to convince Bryan to switch to exoplanet research permanently, he is “excited that there is the opportunity to keep on working together.”

Once he finishes his PhD, Bryan plans on continuing in academia as a professor running a research lab, shifting his focus onto volcano seismology and improving instrumentation for the field. He’s open to the possibility of taking his findings on Earth and applying them to volcanoes on other planetary bodies, such as those found on Venus and Jupiter’s moon Io.

“I’d like to be the bridge between those two things,” he says.

de MIT News https://ift.tt/xec754d

MIT OpenCourseWare sparks the joy of deep understanding

From a young age, Doğa Kürkçüoğlu heard his father, a math teacher, say that learning should be about understanding and real-world applications rather than memorization. But it wasn’t until he began exploring MIT OpenCourseWare in 2004 that Kürkçüoğlu experienced what it means to truly understand complex subject matter.

“MIT professors showed me how to look at a concept from different angles that I hadn’t before, and that helped me internalize information,” says Kürkçüoğlu, who turned to MIT OpenCourseWare to supplement what he was learning as an undergraduate studying physics. “Once I understood techniques and concepts, I was able to apply them in different disciplines. Even now, there are many equations I don’t have memorized exactly, but because I understand the underlying ideas, I can derive them myself in just a few minutes.”

Though there was a point in his life when friends and classmates thought he might pursue music, Kürkçüoğlu — a skilled violinist who currently plays in a jazz band on the side — always had a passion for math and physics and was determined to learn everything he could to pursue the career he imagined for himself.

“Even when I was 4 or 5 years old, if someone asked me, ‘what do you want to be when you grow up?’ I would say a scientist or mathematician,” says Kürkçüoğlu, who is now a staff scientist at Fermilab in the Superconducting Quantum Materials and Systems Center. Fermilab is the U.S. Department of Energy laboratory for particle physics and accelerator research. “I feel lucky that I actually get to do the job I imagined as a little kid,” Kürkçüoğlu says.

OpenCourseWare and other resources from MIT Open Learning — including courses, lectures, written guides, and problem sets — played an important role in Kürkçüoğlu’s learning journey and career. He turned to these open educational resources throughout his undergraduate studies at Marmara University in Turkey. When he completed his degree in 2008, Kürkçüoğlu set his sights on a PhD. He says he felt ready to dive right into doctoral-level research thanks to so many MIT OpenCourseWare lectures, courses, and study guides. He started a PhD program at Georgia Tech, where his research focused on theoretical condensed matter physics with ultra-cold atoms.

“Without OpenCourseWare, I could not have done that,” he says, adding that he considers himself “an honorary MIT graduate.”

Memorable courses include particle physics with Iain W. Stewart, the Otto (1939) and Jane Morningstar Professorship in Science Professor of Physics and director of the Center for Theoretical Physics; and Statistical Mechanics of Fields with Mehran Kardar, professor of physics. Learning from Kardar felt especially apt, because Kürkçüoğlu’s undergraduate advisor, Nihat Berker, was Kardar’s PhD advisor. Berker is also emeritus professor of physics at MIT.

Once he completed his PhD in 2015, Kürkçüoğlu spent time as an assistant professor at Georgia Southern University and a postdoc at Los Alamos National Laboratory. He joined Fermilab in 2020. There, he works on quantum theory and quantum algorithms. He enjoys the research-focused atmosphere of a national laboratory, where teams of scientists are working toward tangible goals.

When he was teaching, though, he encouraged his students to check out Open Learning resources.

“I would tell them, first of all, to have fun. Learning should be fun — another idea that my father always encouraged as a math teacher. With OpenCourseWare, you can get a new perspective on something you already know about, or open a course that can expand your horizons,” Kürkçüoğlu says. “Depending on where you start, it might take you an hour, a week, or a month to fully understand something. Once you understand, it’s yours. It is a different kind of joy to actually, truly understand.”

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A wobble from Mars could be sign of dark matter, MIT study finds

In a new study, MIT physicists propose that if most of the dark matter in the universe is made up of microscopic primordial black holes — an idea first proposed in the 1970s — then these gravitational dwarfs should zoom through our solar system at least once per decade. A flyby like this, the researchers predict, would introduce a wobble into Mars’ orbit, to a degree that today’s technology could actually detect.

Such a detection could lend support to the idea that primordial black holes are a primary source of dark matter throughout the universe.

“Given decades of precision telemetry, scientists know the distance between Earth and Mars to an accuracy of about 10 centimeters,” says study author David Kaiser, professor of physics and the Germeshausen Professor of the History of Science at MIT. “We’re taking advantage of this highly instrumented region of space to try and look for a small effect. If we see it, that would count as a real reason to keep pursuing this delightful idea that all of dark matter consists of black holes that were spawned in less than a second after the Big Bang and have been streaming around the universe for 14 billion years.”

Kaiser and his colleagues report their findings today in the journal Physical Review D. The study’s co-authors are lead author Tung Tran ’24, who is now a graduate student at Stanford University; Sarah Geller ’12, SM ’17, PhD ’23, who is now a postdoc at the University of California at Santa Cruz; and MIT Pappalardo Fellow Benjamin Lehmann.

Beyond particles

Less than 20 percent of all physical matter is made from visible stuff, from stars and planets, to the kitchen sink. The rest is composed of dark matter, a hypothetical form of matter that is invisible across the entire electromagnetic spectrum yet is thought to pervade the universe and exert a gravitational force large enough to affect the motion of stars and galaxies.

Physicists have erected detectors on Earth to try and spot dark matter and pin down its properties. For the most part, these experiments assume that dark matter exists as a form of exotic particle that might scatter and decay into observable particles as it passes through a given experiment. But so far, such particle-based searches have come up empty.

In recent years, another possibility, first introduced in the 1970s, has regained traction: Rather than taking on a particle form, dark matter could exist as microscopic, primordial black holes that formed in the first moments following the Big Bang. Unlike the astrophysical black holes that form from the collapse of old stars, primordial black holes would have formed from the collapse of dense pockets of gas in the very early universe and would have scattered across the cosmos as the universe expanded and cooled.

These primordial black holes would have collapsed an enormous amount of mass into a tiny space. The majority of these primordial black holes could be as small as a single atom and as heavy as the largest asteroids. It would be conceivable, then, that such tiny giants could exert a gravitational force that could explain at least a portion of dark matter. For the MIT team, this possibility raised an initially frivolous question.

“I think someone asked me what would happen if a primordial black hole passed through a human body,” recalls Tung, who did a quick pencil-and-paper calculation to find that if such a black hole zinged within 1 meter of a person, the force of the black hole would push the person 6 meters, or about 20 feet away in a single second. Tung also found that the odds were astronomically unlikely that a primordial black hole would pass anywhere near a person on Earth.

Their interest piqued, the researchers took Tung’s calculations a step further, to estimate how a black hole flyby might affect much larger bodies such as the Earth and the moon.

“We extrapolated to see what would happen if a black hole flew by Earth and caused the moon to wobble by a little bit,” Tung says. “The numbers we got were not very clear. There are many other dynamics in the solar system that could act as some sort of friction to cause the wobble to dampen out.”

Close encounters

To get a clearer picture, the team generated a relatively simple simulation of the solar system that incorporates the orbits and gravitational interactions between all the planets, and some of the largest moons.

“State-of-the-art simulations of the solar system include more than a million objects, each of which has a tiny residual effect,” Lehmann notes. “But even modeling two dozen objects in a careful simulation, we could see there was a real effect that we could dig into.”

The team worked out the rate at which a primordial black hole should pass through the solar system, based on the amount of dark matter that is estimated to reside in a given region of space and the mass of a passing black hole, which in this case, they assumed to be as massive as the largest asteroids in the solar system, consistent with other astrophysical constraints.

“Primordial black holes do not live in the solar system. Rather, they’re streaming through the universe, doing their own thing,” says co-author Sarah Geller. “And the probability is, they’re going through the inner solar system at some angle once every 10 years or so.”

Given this rate, the researchers simulated various asteroid-mass black holes flying through the solar system, from various angles, and at velocities of about 150 miles per second. (The directions and speeds come from other studies of the distribution of dark matter throughout our galaxy.) They zeroed in on those flybys that appeared to be “close encounters,” or instances that caused some sort of effect in surrounding objects. They quickly found that any effect in the Earth or the moon was too uncertain to pin to a particular black hole. But Mars seemed to offer a clearer picture.

The researchers found that if a primordial black hole were to pass within a few hundred million miles of Mars, the encounter would set off a “wobble,” or a slight deviation in Mars’ orbit. Within a few years of such an encounter, Mars’ orbit should shift by about a meter — an incredibly small wobble, given the planet is more than 140 million miles from Earth. And yet, this wobble could be detected by the various high-precision instruments that are monitoring Mars today.

If such a wobble were detected in the next couple of decades, the researchers acknowledge there would still be much work needed to confirm that the push came from a passing black hole rather than a run-of-the-mill asteroid.

“We need as much clarity as we can of the expected backgrounds, such as the typical speeds and distributions of boring space rocks, versus these primordial black holes,” Kaiser notes. “Luckily for us, astronomers have been tracking ordinary space rocks for decades as they have flown through our solar system, so we could calculate typical properties of their trajectories and begin to compare them with the very different types of paths and speeds that primordial black holes should follow.”

To help with this, the researchers are exploring the possibility of a new collaboration with a group that has extensive expertise simulating many more objects in the solar system.

“We are now working to simulate a huge number of objects, from planets to moons and rocks, and how they’re all moving over long time scales,” Geller says. “We want to inject close encounter scenarios, and look at their effects with higher precision.”

“It’s a very neat test they’ve proposed, and it could tell us if the closest black hole is closer than we realize,” says Matt Caplan, associate professor of physics at Illinois State University, who was not involved in the study. “I should emphasize there’s a little bit of luck involved too. Whether or not a search finds a loud and clear signal depends on the exact path a wandering black hole takes through the solar system. Now that they’ve checked this idea with simulations, they have to do the hard part — checking the real data.”

This work was supported in part by the U.S. Department of Energy and the U.S. National Science Foundation, which includes an NSF Mathematical and Physical Sciences postdoctoral fellowship.

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Enhancing LLM collaboration for smarter, more efficient solutions

Ever been asked a question you only knew part of the answer to? To give a more informed response, your best move would be to phone a friend with more knowledge on the subject.

This collaborative process can also help large language models (LLMs) improve their accuracy. Still, it’s been difficult to teach LLMs to recognize when they should collaborate with another model on an answer. Instead of using complex formulas or large amounts of labeled data to spell out where models should work together, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have envisioned a more organic approach.

Their new algorithm, called “Co-LLM,” can pair a general-purpose base LLM with a more specialized model and help them work together. As the former crafts an answer, Co-LLM reviews each word (or token) within its response to see where it can call upon a more accurate answer from the expert model. This process leads to more accurate replies to things like medical prompts and math and reasoning problems. Since the expert model is not needed at each iteration, this also leads to more efficient response generation.

To decide when a base model needs help from an expert model, the framework uses machine learning to train a “switch variable,” or a tool that can indicate the competence of each word within the two LLMs’ responses. The switch is like a project manager, finding areas where it should call in a specialist. If you asked Co-LLM to name some examples of extinct bear species, for instance, two models would draft answers together. The general-purpose LLM begins to put together a reply, with the switch variable intervening at the parts where it can slot in a better token from the expert model, such as adding the year when the bear species became extinct.

“With Co-LLM, we’re essentially training a general-purpose LLM to ‘phone’ an expert model when needed,” says Shannon Shen, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate who’s a lead author on a new paper about the approach. “We use domain-specific data to teach the base model about its counterpart’s expertise in areas like biomedical tasks and math and reasoning questions. This process automatically finds the parts of the data that are hard for the base model to generate, and then it instructs the base model to switch to the expert LLM, which was pretrained on data from a similar field. The general-purpose model provides the ‘scaffolding’ generation, and when it calls on the specialized LLM, it prompts the expert to generate the desired tokens. Our findings indicate that the LLMs learn patterns of collaboration organically, resembling how humans recognize when to call upon an expert to fill in the blanks.”

A combination of flexibility and factuality

Imagine asking a general-purpose LLM to name the ingredients of a specific prescription drug. It may reply incorrectly, necessitating the expertise of a specialized model.

To showcase Co-LLM’s flexibility, the researchers used data like the BioASQ medical set to couple a base LLM with expert LLMs in different domains, like the Meditron model, which is pretrained on unlabeled medical data. This enabled the algorithm to help answer inquiries a biomedical expert would typically receive, such as naming the mechanisms causing a particular disease.

For example, if you asked a simple LLM alone to name the ingredients of a specific prescription drug, it may reply incorrectly. With the added expertise of a model that specializes in biomedical data, you’d get a more accurate answer. Co-LLM also alerts users where to double-check answers.

Another example of Co-LLM’s performance boost: When tasked with solving a math problem like “a3 · a2 if a=5,” the general-purpose model incorrectly calculated the answer to be 125. As Co-LLM trained the model to collaborate more with a large math LLM called Llemma, together they determined that the correct solution was 3,125.

Co-LLM gave more accurate replies than fine-tuned simple LLMs and untuned specialized models working independently. Co-LLM can guide two models that were trained differently to work together, whereas other effective LLM collaboration approaches, such as “Proxy Tuning,” need all of their component models to be trained similarly. Additionally, this baseline requires each model to be used simultaneously to produce the answer, whereas MIT’s algorithm simply activates its expert model for particular tokens, leading to more efficient generation.

When to ask the expert

The MIT researchers’ algorithm highlights that imitating human teamwork more closely can increase accuracy in multi-LLM collaboration. To further elevate its factual precision, the team may draw from human self-correction: They’re considering a more robust deferral approach that can backtrack when the expert model doesn’t give a correct response. This upgrade would allow Co-LLM to course-correct so the algorithm can still give a satisfactory reply.

The team would also like to update the expert model (via only training the base model) when new information is available, keeping answers as current as possible. This would allow Co-LLM to pair the most up-to-date information with strong reasoning power. Eventually, the model could assist with enterprise documents, using the latest information it has to update them accordingly. Co-LLM could also train small, private models to work with a more powerful LLM to improve documents that must remain within the server.

“Co-LLM presents an interesting approach for learning to choose between two models to improve efficiency and performance,” says Colin Raffel, associate professor at the University of Toronto and an associate research director at the Vector Institute, who wasn’t involved in the research. “Since routing decisions are made at the token-level, Co-LLM provides a granular way of deferring difficult generation steps to a more powerful model. The unique combination of model-token-level routing also provides a great deal of flexibility that similar methods lack. Co-LLM contributes to an important line of work that aims to develop ecosystems of specialized models to outperform expensive monolithic AI systems.”

Shen wrote the paper with four other CSAIL affiliates: PhD student Hunter Lang ’17, MEng ’18; former postdoc and Apple AI/ML researcher Bailin Wang; MIT assistant professor of electrical engineering and computer science Yoon Kim, and professor and Jameel Clinic member David Sontag PhD ’10, who are both part of MIT-IBM Watson AI Lab. Their research was supported, in part, by the National Science Foundation, The National Defense Science and Engineering Graduate (NDSEG) Fellowship, MIT-IBM Watson AI Lab, and Amazon. Their work was presented at the Annual Meeting of the Association for Computational Linguistics.

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Finding some stability in adaptable brains

One of the brain’s most celebrated qualities is its adaptability. Changes to neural circuits, whose connections are continually adjusted as we experience and interact with the world, are key to how we learn. But to keep knowledge and memories intact, some parts of the circuitry must be resistant to this constant change.

“Brains have figured out how to navigate this landscape of balancing between stability and flexibility, so that you can have new learning and you can have lifelong memory,” says neuroscientist Mark Harnett, an investigator at MIT’s McGovern Institute for Brain Research. In the Aug. 27 issue of the journal Cell Reports, Harnett and his team show how individual neurons can contribute to both parts of this vital duality. By studying the synapses through which pyramidal neurons in the brain’s sensory cortex communicate, they have learned how the cells preserve their understanding of some of the world’s most fundamental features, while also maintaining the flexibility they need to adapt to a changing world.

Visual connections

Pyramidal neurons receive input from other neurons via thousands of connection points. Early in life, these synapses are extremely malleable; their strength can shift as a young animal takes in visual information and learns to interpret it. Most remain adaptable into adulthood, but Harnett’s team discovered that some of the cells’ synapses lose their flexibility when the animals are less than a month old. Having both stable and flexible synapses means these neurons can combine input from different sources to use visual information in flexible ways.

Postdoc Courtney Yaeger took a close look at these unusually stable synapses, which cluster together along a narrow region of the elaborately branched pyramidal cells. She was interested in the connections through which the cells receive primary visual information, so she traced their connections with neurons in a vision-processing center of the brain’s thalamus called the dorsal lateral geniculate nucleus (dLGN).

The long extensions through which a neuron receives signals from other cells are called dendrites, and they branch of from the main body of the cell into a tree-like structure. Spiny protrusions along the dendrites form the synapses that connect pyramidal neurons to other cells. Yaeger’s experiments showed that connections from the dLGN all led to a defined region of the pyramidal cells — a tight band within what she describes as the trunk of the dendritic tree.

Yaeger found several ways in which synapses in this region — formally known as the apical oblique dendrite domain — differ from other synapses on the same cells. “They’re not actually that far away from each other, but they have completely different properties,” she says.

Stable synapses

In one set of experiments, Yaeger activated synapses on the pyramidal neurons and measured the effect on the cells’ electrical potential. Changes to a neuron’s electrical potential generate the impulses the cells use to communicate with one another. It is common for a synapse’s electrical effects to amplify when synapses nearby are also activated. But when signals were delivered to the apical oblique dendrite domain, each one had the same effect, no matter how many synapses were stimulated. Synapses there don’t interact with one another at all, Harnett says. “They just do what they do. No matter what their neighbors are doing, they all just do kind of the same thing.”

The team was also able to visualize the molecular contents of individual synapses. This revealed a surprising lack of a certain kind of neurotransmitter receptor, called NMDA receptors, in the apical oblique dendrites. That was notable because of NMDA receptors’ role in mediating changes in the brain. “Generally when we think about any kind of learning and memory and plasticity, it’s NMDA receptors that do it,” Harnett says. “That is the by far most common substrate of learning and memory in all brains.”

When Yaeger stimulated the apical oblique synapses with electricity, generating patterns of activity that would strengthen most synapses, the team discovered a consequence of the limited presence of NMDA receptors. The synapses’ strength did not change. “There’s no activity-dependent plasticity going on there, as far as we have tested,” Yaeger says.

That makes sense, the researchers say, because the cells’ connections from the thalamus relay primary visual information detected by the eyes. It is through these connections that the brain learns to recognize basic visual features like shapes and lines.

“These synapses are basically a robust, high-fidelity readout of this visual information,” Harnett explains. “That’s what they’re conveying, and it’s not context-sensitive. So it doesn’t matter how many other synapses are active, they just do exactly what they’re going to do, and you can’t modify them up and down based on activity. So they’re very, very stable.”

“You actually don’t want those to be plastic,” adds Yaeger. "Can you imagine going to sleep and then forgetting what a vertical line looks like? That would be disastrous.” 

By conducting the same experiments in mice of different ages, the researchers determined that the synapses that connect pyramidal neurons to the thalamus become stable a few weeks after young mice first open their eyes. By that point, Harnett says, they have learned everything they need to learn. On the other hand, if mice spend the first weeks of their lives in the dark, the synapses never stabilize — further evidence that the transition depends on visual experience.

The team’s findings not only help explain how the brain balances flexibility and stability; they could help researchers teach artificial intelligence how to do the same thing. Harnett says artificial neural networks are notoriously bad at this: when an artificial neural network that does something well is trained to do something new, it almost always experiences “catastrophic forgetting” and can no longer perform its original task. Harnett’s team is exploring how they can use what they’ve learned about real brains to overcome this problem in artificial networks.

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A new way to reprogram immune cells and direct them toward anti-tumor immunity

A collaboration between four MIT groups, led by principal investigators Laura L. Kiessling, Jeremiah A. Johnson, Alex K. Shalek, and Darrell J. Irvine, in conjunction with a group at Georgia Tech led by M.G. Finn, has revealed a new strategy for enabling immune system mobilization against cancer cells. The work, which appears today in ACS Nano, produces exactly the type of anti-tumor immunity needed to function as a tumor vaccine — both prophylactically and therapeutically.

Cancer cells can look very similar to the human cells from which they are derived. In contrast, viruses, bacteria, and fungi carry carbohydrates on their surfaces that are markedly different from those of human carbohydrates. Dendritic cells — the immune system’s best antigen-presenting cells — carry proteins on their surfaces that help them recognize these atypical carbohydrates and bring those antigens inside of them. The antigens are then processed into smaller peptides and presented to the immune system for a response. Intriguingly, some of these carbohydrate proteins can also collaborate to direct immune responses. This work presents a strategy for targeting those antigens to the dendritic cells that results in a more activated, stronger immune response.

Tackling tumors’ tenacity

The researchers’ new strategy shrouds the tumor antigens with foreign carbohydrates and co-delivers them with single-stranded RNA so that the dendritic cells can be programmed to recognize the tumor antigens as a potential threat. The researchers targeted the lectin (carbohydrate-binding protein) DC-SIGN because of its ability to serve as an activator of dendritic cell immunity. They decorated a virus-like particle (a particle composed of virus proteins assembled onto a piece of RNA that is noninfectious because its internal RNA is not from the virus) with DC-binding carbohydrate derivatives. The resulting glycan-costumed virus-like particles display unique sugars; therefore, the dendritic cells recognize them as something they need to attack.

“On the surface of the dendritic cells are carbohydrate binding proteins called lectins that combine to the sugars on the surface of bacteria or viruses, and when they do that they penetrate the membrane,” explains Kiessling, the paper’s senior author. “On the cell, the DC-SIGN gets clustered upon binding the virus or bacteria and that promotes internalization. When a virus-like particle gets internalized, it starts to fall apart and releases its RNA.” The toll-like receptor (bound to RNA) and DC-SIGN (bound to the sugar decoration) can both signal to activate the immune response.

Once the dendritic cells have sounded the alarm of a foreign invasion, a robust immune response is triggered that is significantly stronger than the immune response that would be expected with a typical untargeted vaccine. When an antigen is encountered by the dendritic cells, they send signals to T cells, the next cell in the immune system, to give different responses depending on what pathways have been activated in the dendritic cells.

Advancing cancer vaccine development

The activity of a potential vaccine developed in line with this new research is twofold. First, the vaccine glycan coat binds to lectins, providing a primary signal. Then, binding to toll-like receptors elicits potent immune activation.

The Kiessling, Finn, and Johnson groups had previously identified a synthetic DC-SIGN binding group that directed cellular immune responses when used to decorate virus-like particles. But it was unclear whether this method could be utilized as an anticancer vaccine. Collaboration between researchers in the labs at MIT and Georgia Tech demonstrated that in fact, it could.

Valerie Lensch, a chemistry PhD student from MIT’s Program in Polymers and Soft Matter and a joint member of the Kiessling and Johnson labs, took the preexisting strategy and tested it as an anticancer vaccine, learning a great deal about immunology in order to do so.

“We have developed a modular vaccine platform designed to drive antigen-specific cellular immune responses,” says Lensch. “This platform is not only pivotal in the fight against cancer, but also offers significant potential for combating challenging intracellular pathogens, including malaria parasites, HIV, and Mycobacterium tuberculosis. This technology holds promise for tackling a range of diseases where vaccine development has been particularly challenging.”

Lensch and her fellow researchers conducted in vitro experiments with extensive iterations of these glycan-costumed virus-like particles before identifying a design that demonstrated potential for success. Once that was achieved, the researchers were able to move on to an in vivo model, an exciting milestone for their research.

Adele Gabba, a postdoc in the Kiessling Lab, conducted the in vivo experiments with Lensch, and Robert Hincapie, who conducted his PhD studies with Professor M.G. Finn at Georgia Tech, built and decorated the virus-like particles with a series of glycans that were sent to him from the researchers at MIT.

“We are discovering that carbohydrates act like a language that cells use to communicate and direct the immune system,” says Gabba. “It's thrilling that we have begun to decode this language and can now harness it to reshape immune responses.”

“The design principles behind this vaccine are rooted in extensive fundamental research conducted by previous graduate student and postdoctoral researchers over many years, focusing on optimizing lectin engagement and understanding the roles of lectins in immunity,” says Lensch. “It has been exciting to witness the translation of these concepts into therapeutic platforms across various applications.”

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