Understanding reality through algorithms

Although Fernanda De La Torre still has several years left in her graduate studies, she’s already dreaming big when it comes to what the future has in store for her.

“I dream of opening up a school one day where I could bring this world of understanding of cognition and perception into places that would never have contact with this,” she says.

It’s that kind of ambitious thinking that’s gotten De La Torre, a doctoral student in MIT’s Department of Brain and Cognitive Sciences, to this point. A recent recipient of the prestigious Paul and Daisy Soros Fellowship for New Americans, De La Torre has found at MIT a supportive, creative research environment that’s allowed her to delve into the cutting-edge science of artificial intelligence. But she’s still driven by an innate curiosity about human imagination and a desire to bring that knowledge to the communities in which she grew up.

An unconventional path to neuroscience

De La Torre’s first exposure to neuroscience wasn’t in the classroom, but in her daily life. As a child, she watched her younger sister struggle with epilepsy. At 12, she crossed into the United States from Mexico illegally to reunite with her mother, exposing her to a whole new language and culture. Once in the States, she had to grapple with her mother’s shifting personality in the midst of an abusive relationship. “All of these different things I was seeing around me drove me to want to better understand how psychology works,” De La Torre says, “to understand how the mind works, and how it is that we can all be in the same environment and feel very different things.”

But finding an outlet for that intellectual curiosity was challenging. As an undocumented immigrant, her access to financial aid was limited. Her high school was also underfunded and lacked elective options. Mentors along the way, though, encouraged the aspiring scientist, and through a program at her school, she was able to take community college courses to fulfill basic educational requirements.

It took an inspiring amount of dedication to her education, but De La Torre made it to Kansas State University for her undergraduate studies, where she majored in computer science and math. At Kansas State, she was able to get her first real taste of research. “I was just fascinated by the questions they were asking and this entire space I hadn’t encountered,” says De La Torre of her experience working in a visual cognition lab and discovering the field of computational neuroscience.

Although Kansas State didn’t have a dedicated neuroscience program, her research experience in cognition led her to a machine learning lab led by William Hsu, a computer science professor. There, De La Torre became enamored by the possibilities of using computation to model the human brain. Hsu’s support also convinced her that a scientific career was a possibility. “He always made me feel like I was capable of tackling big questions,” she says fondly.

With the confidence imparted in her at Kansas State, De La Torre came to MIT in 2019 as a post-baccalaureate student in the lab of Tomaso Poggio, the Eugene McDermott Professor of Brain and Cognitive Sciences and an investigator at the McGovern Institute for Brain Research. With Poggio, also the director of the Center for Brains, Minds and Machines, De La Torre began working on deep-learning theory, an area of machine learning focused on how artificial neural networks modeled on the brain can learn to recognize patterns and learn.

“It’s a very interesting question because we’re starting to use them everywhere,” says De La Torre of neural networks, listing off examples from self-driving cars to medicine. “But, at the same time, we don’t fully understand how these networks can go from knowing nothing and just being a bunch of numbers to outputting things that make sense.”

Her experience as a post-bac was De La Torre’s first real opportunity to apply the technical computer skills she developed as an undergraduate to neuroscience. It was also the first time she could fully focus on research. “That was the first time that I had access to health insurance and a stable salary. That was, in itself, sort of life-changing,” she says. “But on the research side, it was very intimidating at first. I was anxious, and I wasn’t sure that I belonged here.”

Fortunately, De La Torre says she was able to overcome those insecurities, both through a growing unabashed enthusiasm for the field and through the support of Poggio and her other colleagues in MIT’s Department of Brain and Cognitive Sciences. When the opportunity came to apply to the department’s PhD program, she jumped on it. “It was just knowing these kinds of mentors are here and that they cared about their students,” says De La Torre of her decision to stay on at MIT for graduate studies. “That was really meaningful.”

Expanding notions of reality and imagination

In her two years so far in the graduate program, De La Torre’s work has expanded the understanding of neural networks and their applications to the study of the human brain. Working with Guangyu Robert Yang, an associate investigator at the McGovern Institute and an assistant professor in the departments of Brain and Cognitive Sciences and Electrical Engineering and Computer Sciences, she’s engaged in what she describes as more philosophical questions about how one develops a sense of self as an independent being. She’s interested in how that self-consciousness develops and why it might be useful.

De La Torre’s primary advisor, though, is Professor Josh McDermott, who leads the Laboratory for Computational Audition. With McDermott, De La Torre is attempting to understand how the brain integrates vision and sound. While combining sensory inputs may seem like a basic process, there are many unanswered questions about how our brains combine multiple signals into a coherent impression, or percept, of the world. Many of the questions are raised by audiovisual illusions in which what we hear changes what we see. For example, if one sees a video of two discs passing each other, but the clip contains the sound of a collision, the brain will perceive that the discs are bouncing off, rather than passing through each other. Given an ambiguous image, that simple auditory cue is all it takes to create a different perception of reality.

There’s something interesting happening where our brains are receiving two signals telling us different things and, yet, we have to combine them somehow to make sense of the world.

De La Torre is using behavioral experiments to probe how the human brain makes sense of multisensory cues to construct a particular perception. To do so, she’s created various scenes of objects interacting in 3D space over different sounds, asking research participants to describe characteristics of the scene. For example, in one experiment, she combines visuals of a block moving across a surface at different speeds with various scraping sounds, asking participants to estimate how rough the surface is. Eventually she hopes to take the experiment into virtual reality, where participants will physically push blocks in response to how rough they perceive the surface to be, rather than just reporting on what they experience.

Once she’s collected data, she’ll move into the modeling phase of the research, evaluating whether multisensory neural networks perceive illusions the way humans do. “What we want to do is model exactly what’s happening,” says De La Torre. “How is it that we’re receiving these two signals, integrating them and, at the same time, using all of our prior knowledge and inferences of physics to really make sense of the world?”

Although her two strands of research with Yang and McDermott may seem distinct, she sees clear connections between the two. Both projects are about grasping what artificial neural networks are capable of and what they tell us about the brain. At a more fundamental level, she says that how the brain perceives the world from different sensory cues might be part of what gives people a sense of self. Sensory perception is about constructing a cohesive, unitary sense of the world from multiple sources of sensory data. Similarly, she argues, “the sense of self is really a combination of actions, plans, goals, emotions, all of these different things that are components of their own, but somehow create a unitary being.”

It’s a fitting sentiment for De La Torre, who has been working to make sense of and integrate different aspects of her own life. Working in the Computational Audition lab, for example, she’s started experimenting with combining electronic music with folk music from her native Mexico, connecting her “two worlds,” as she says. Having the space to undertake those kinds of intellectual explorations, and colleagues who encourage it, is one of De La Torre’s favorite parts of MIT.

“Beyond professors, there’s also a lot of students whose way of thinking just amazes me,” she says. “I see a lot of goodness and excitement for science and a little bit of — it’s not nerdiness, but a love for very niche things — and I just kind of love that.”

Nidhi Seethapathi

Science in Motion

The computational models that Seethapathi builds in her lab aim to predict how humans will move under different conditions. If a person is placed in an unfamiliar environment and asked to navigate a course under time pressure, what path will they take? How will they move their limbs, and what forces will they exert? How will their movements change as they become more comfortable on the terrain?

Seethapathi uses the principles of robotics to build models that answer these questions, then tests them by placing real people in the same scenarios and monitoring their movements. Currently, most of these tests take place in her lab, where subjects are often limited to simple tasks like walking on a treadmill. As she expands her models to predict more complex movements, she will begin monitoring people’s activity in the real world, over longer time periods than laboratory experiments typically allow. Ultimately, Seethapathi hopes her findings will inform the way doctors, therapists, and engineers help patients regain control over their movements after an injury or due to a movement disorder.

Modeling the social mind

Typically, it would take two graduate students to do the research that Setayesh Radkani is doing.

Driven by an insatiable curiosity about the human mind, she is working on two PhD thesis projects in two different cognitive neuroscience labs at MIT. For one, she is studying punishment as a social tool to influence others. For the other, she is uncovering the neural processes underlying social learning — that is, learning from others. By piecing together these two research programs, Radkani is hoping to gain a better understanding of the mechanisms underpinning social influence in the mind and brain.

Radkani lived in Iran for most of her life, growing up alongside her younger brother in Tehran. The two spent a lot of time together and have long been each other’s best friends. Her father is a civil engineer, and her mother is a midwife. Her parents always encouraged her to explore new things and follow her own path, even if it wasn’t quite what they imagined for her. And her uncle helped cultivate her sense of curiosity, teaching her to “always ask why” as a way to understand how the world works.

Growing up, Radkani most loved learning about human psychology and using math to model the world around her. But she thought it was impossible to combine her two interests. Prioritizing math, she pursued a bachelor’s degree in electrical engineering at the Sharif University of Technology in Iran.

Then, late in her undergraduate studies, Radkani took a psychology course and discovered the field of cognitive neuroscience, in which scientists mathematically model the human mind and brain. She also spent a summer working in a computational neuroscience lab at the Swiss Federal Institute of Technology in Lausanne. Seeing a way to combine her interests, she decided to pivot and pursue the subject in graduate school.

An experience leading a project in her engineering ethics course during her final year of undergrad further helped her discover some of the questions that would eventually form the basis of her PhD. The project investigated why some students cheat and how to change this.

“Through this project I learned how complicated it is to understand the reasons that people engage in immoral behavior, and even more complicated than that is how to devise policies and react in these situations in order to change people’s attitudes,” Radkani says. “It was this experience that made me realize that I’m interested in studying the human social and moral mind.”

She began looking into social cognitive neuroscience research and stumbled upon a relevant TED talk by Rebecca Saxe, the John W. Jarve Professor in Brain and Cognitive Sciences at MIT, who would eventually become one of Radkani’s research advisors. Radkani knew immediately that she wanted to work with Saxe. But she needed to first get into the BCS PhD program at MIT, a challenging obstacle given her minimal background in the field.

After two application cycles and a year’s worth of graduate courses in cognitive neuroscience, Radkani was accepted into the program. But to come to MIT, she had to leave her family behind. Coming from Iran, Radkani has a single-entry visa, making it difficult for her to travel outside the U.S. She hasn’t been able to visit her family since starting her PhD and won’t be able to until at least after she graduates. Her visa also limits her research contributions, restricting her from attending conferences outside the U.S. “That is definitely a huge burden on my education and on my mental health,” she says.

Still, Radkani is grateful to be at MIT, indulging her curiosity in the human social mind. And she’s thankful for her supportive family, who she calls over FaceTime every day.

Modeling how people think about punishment

In Saxe’s lab, Radkani is researching how people approach and react to punishment, through behavioral studies and neuroimaging. By synthesizing these findings, she’s developing a computational model of the mind that characterizes how people make decisions in situations involving punishment, such as when a parent disciplines a child, when someone punishes their romantic partner, or when the criminal justice system sentences a defendant. With this model, Radkani says she hopes to better understand “when and why punishment works in changing behavior and influencing beliefs about right and wrong, and why sometimes it fails.”

Punishment isn’t a new research topic in cognitive neuroscience, Radkani says, but in previous studies, scientists have often only focused on people’s behavior in punitive situations and haven’t considered the thought processes that underlie those behaviors. Characterizing these thought processes, though, is key to understanding whether punishment in a situation can be effective in changing people’s attitudes.

People bring their prior beliefs into a punitive situation. Apart from moral beliefs about the appropriateness of different behaviors, “you have beliefs about the characteristics of the people involved, and you have theories about their intentions and motivations,” Radkani says. “All those come together to determine what you do or how you are influenced by punishment,” given the circumstances. Punishers decide a suitable punishment based on their interpretation of the situation, in light of their beliefs. Targets of punishment then decide whether they’ll change their attitude as a result of the punishment, depending on their own beliefs. Even outside observers make decisions, choosing whether to keep or change their moral beliefs based on what they see.

To capture these decision-making processes, Radkani is developing a computational model of the mind for punitive situations. The model mathematically represents people’s beliefs and how they interact with certain features of the situation to shape their decisions. The model then predicts a punisher’s decisions, and how punishment will influence the target and observers. Through this model, Radkani will provide a foundational understanding of how people think in various punitive situations.

Researching the neural mechanisms of social learning

In parallel, working in the lab of Professor Mehrdad Jazayeri, Radkani is studying social learning, uncovering its underlying neural processes. Through social learning, people learn from other people’s experiences and decisions, and incorporate this socially acquired knowledge into their own decisions or beliefs.

Humans are extraordinary in their social learning abilities, however our primary form of learning, shared by all other animals, is learning from self-experience. To investigate how learning from others is similar to or different from learning from our own experiences, Radkani has designed a two-player video game that involves both types of learning. During the game, she and her collaborators in Jazayeri’s lab record neural activity in the brain. By analyzing these neural measurements, they plan to uncover the computations carried out by neural circuits during social learning, and compare those to learning from self-experience.

Radkani first became curious about this comparison as a way to understand why people sometimes draw contrasting conclusions from very similar situations. “For example, if I get Covid from going to a restaurant, I’ll blame the restaurant and say it was not clean,” Radkani says. “But if I hear the same thing happen to my friend, I’ll say it’s because they were not careful.” Radkani wanted to know the root causes of this mismatch in how other people’s experiences affect our beliefs and judgements differently from our own similar experiences, particularly because it can lead to “errors that color the way that we judge other people,” she says.

By combining her two research projects, Radkani hopes to better understand how social influence works, particularly in moral situations. From there, she has a slew of research questions that she’s eager to investigate, including: How do people choose who to trust? And which types of people tend to be the most influential? As Radkani’s research grows, so does her curiosity.

Whether speaking Turkish or Norwegian, the brain’s language network looks the same

Over several decades, neuroscientists have created a well-defined map of the brain’s “language network,” or the regions of the brain that are specialized for processing language. Found primarily in the left hemisphere, this network includes regions within Broca’s area, as well as in other parts of the frontal and temporal lobes.

However, the vast majority of those mapping studies have been done in English speakers as they listened to or read English texts. MIT neuroscientists have now performed brain imaging studies of speakers of 45 different languages. The results show that the speakers’ language networks appear to be essentially the same as those of native English speakers.

The findings, while not surprising, establish that the location and key properties of the language network appear to be universal. The work also lays the groundwork for future studies of linguistic elements that would be difficult or impossible to study in English speakers because English doesn’t have those features.

“This study is very foundational, extending some findings from English to a broad range of languages,” says Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience at MIT and a member of MIT’s McGovern Institute for Brain Research. “The hope is that now that we see that the basic properties seem to be general across languages, we can ask about potential differences between languages and language families in how they are implemented in the brain, and we can study phenomena that don’t really exist in English.”

Fedorenko is the senior author of the study, which appears today in Nature Neuroscience. Saima Malik-Moraleda, a PhD student in the Speech and Hearing Bioscience and Technology program at Harvard University, and Dima Ayyash, a former research assistant, are the lead authors of the paper.

Mapping language networks

The precise locations and shapes of language areas differ across individuals, so to find the language network, researchers ask each person to perform a language task while scanning their brains with functional magnetic resonance imaging (fMRI). Listening to or reading sentences in one’s native language should activate the language network. To distinguish this network from other brain regions, researchers also ask participants to perform tasks that should not activate it, such as listening to an unfamiliar language or solving math problems.

Several years ago, Fedorenko began designing these “localizer” tasks for speakers of languages other than English. While most studies of the language network have used English speakers as subjects, English does not include many features commonly seen in other languages. For example, in English, word order tends to be fixed, while in other languages there is more flexibility in how words are ordered. Many of those languages instead use the addition of morphemes, or segments of words, to convey additional meaning and relationships between words.

“There has been growing awareness for many years of the need to look at more languages, if you want make claims about how language works, as opposed to how English works,” Fedorenko says. “We thought it would be useful to develop tools to allow people to rigorously study language processing in the brain in other parts of the world. There’s now access to brain imaging technologies in many countries, but the basic paradigms that you would need to find the language-responsive areas in a person are just not there.”

For the new study, the researchers performed brain imaging of two speakers of 45 different languages, representing 12 different language families. Their goal was to see if key properties of the language network, such as location, left lateralization, and selectivity, were the same in those participants as in people whose native language is English.

The researchers decided to use “Alice in Wonderland” as the text that everyone would listen to, because it is one of the most widely translated works of fiction in the world. They selected 24 short passages and three long passages, each of which was recorded by a native speaker of the language. Each participant also heard nonsensical passages, which should not activate the language network, and was asked to do a variety of other cognitive tasks that should not activate it.

The team found that the language networks of participants in this study were found in approximately the same brain regions, and had the same selectivity, as those of native speakers of English.

“Language areas are selective,” Malik-Moraleda says. “They shouldn’t be responding during other tasks such as a spatial working memory task, and that was what we found across the speakers of 45 languages that we tested.”

Additionally, language regions that are typically activated together in English speakers, such as the frontal language areas and temporal language areas, were similarly synchronized in speakers of other languages.

The researchers also showed that among all of the subjects, the small amount of variation they saw between individuals who speak different languages was the same as the amount of variation that would typically be seen between native English speakers.

Similarities and differences

While the findings suggest that the overall architecture of the language network is similar across speakers of different languages, that doesn’t mean that there are no differences at all, Fedorenko says. As one example, researchers could now look for differences in speakers of languages that predominantly use morphemes, rather than word order, to help determine the meaning of a sentence.

“There are all sorts of interesting questions you can ask about morphological processing that don’t really make sense to ask in English, because it has much less morphology,” Fedorenko says.

Another possibility is studying whether speakers of languages that use differences in tone to convey different word meanings would have a language network with stronger links to auditory brain regions that encode pitch.

Right now, Fedorenko’s lab is working on a study in which they are comparing the ‘temporal receptive fields’ of speakers of six typologically different languages, including Turkish, Mandarin, and Finnish. The temporal receptive field is a measure of how many words the language processing system can handle at a time, and for English, it has been shown to be six to eight words long.

“The language system seems to be working on chunks of just a few words long, and we’re trying to see if this constraint is universal across these other languages that we’re testing,” Fedorenko says.

The researchers are also working on creating language localizer tasks and finding study participants representing additional languages beyond the 45 from this study.

The research was funded by the National Institutes of Health and research funds from MIT’s Department of Brain and Cognitive Sciences, the McGovern Institute, and the Simons Center for the Social Brain. Malik-Moraleda was funded by a la Caixa Fellowship and a Friends of McGovern fellowship.

Artificial neural networks model face processing in autism

Many of us easily recognize emotions expressed in others’ faces. A smile may mean happiness, while a frown may indicate anger. Autistic people often have a more difficult time with this task. It’s unclear why. But new research, published today in The Journal of Neuroscience, sheds light on the inner workings of the brain to suggest an answer. And it does so using a tool that opens new pathways to modeling the computation in our heads: artificial intelligence.

Researchers have primarily suggested two brain areas where the differences might lie. A region on the side of the primate (including human) brain called the inferior temporal (IT) cortex contributes to facial recognition. Meanwhile, a deeper region called the amygdala receives input from the IT cortex and other sources and helps process emotions.

Kohitij Kar, a research scientist in the lab of MIT Professor James DiCarlo, hoped to zero in on the answer. (DiCarlo, the Peter de Florez Professor in the Department of Brain and Cognitive Sciences, is a member of the McGovern Institute for Brain Research and director of MIT’s Quest for Intelligence.)

Kar began by looking at data provided by two other researchers: Shuo Wang, at Washington University in St. Louis, and Ralph Adolphs, at the California Institute of Technology. In one experiment, they showed images of faces to autistic adults and to neurotypical controls. The images had been generated by software to vary on a spectrum from fearful to happy, and the participants judged, quickly, whether the faces depicted happiness. Compared with controls, autistic adults required higher levels of happiness in the faces to report them as happy.

Modeling the brain

Kar, who is also a member of the Center for Brains, Minds and Machines, trained an artificial neural network, a complex mathematical function inspired by the brain’s architecture, to perform the same task. The network contained layers of units that roughly resemble biological neurons that process visual information. These layers process information as it passes from an input image to a final judgment indicating the probability that the face is happy. Kar found that the network’s behavior more closely matched the neurotypical controls than it did the autistic adults.

The network also served two more interesting functions. First, Kar could dissect it. He stripped off layers and retested its performance, measuring the difference between how well it matched controls and how well it matched autistic adults. This difference was greatest when the output was based on the last network layer. Previous work has shown that this layer in some ways mimics the IT cortex, which sits near the end of the primate brain’s ventral visual processing pipeline. Kar’s results implicate the IT cortex in differentiating neurotypical controls from autistic adults.

The other function is that the network can be used to select images that might be more efficient in autism diagnoses. If the difference between how closely the network matches neurotypical controls versus autistic adults is greater when judging one set of images versus another set of images, the first set could be used in the clinic to detect autistic behavioral traits. “These are promising results,” Kar says. Better models of the brain will come along, “but oftentimes in the clinic, we don’t need to wait for the absolute best product.”

Next, Kar evaluated the role of the amygdala. Again, he used data from Wang and colleagues. They had used electrodes to record the activity of neurons in the amygdala of people undergoing surgery for epilepsy as they performed the face task. The team found that they could predict a person’s judgment based on these neurons’ activity. Kar re-analyzed the data, this time controlling for the ability of the IT-cortex-like network layer to predict whether a face truly was happy. Now, the amygdala provided very little information of its own. Kar concludes that the IT cortex is the driving force behind the amygdala’s role in judging facial emotion.

Noisy networks

Finally, Kar trained separate neural networks to match the judgments of neurotypical controls and autistic adults. He looked at the strengths or “weights” of the connections between the final layers and the decision nodes. The weights in the network matching autistic adults, both the positive or “excitatory” and negative or “inhibitory” weights, were weaker than in the network matching neurotypical controls. This suggests that sensory neural connections in autistic adults might be noisy or inefficient.

To further test the noise hypothesis, which is popular in the field, Kar added various levels of fluctuation to the activity of the final layer in the network modeling autistic adults. Within a certain range, added noise greatly increased the similarity between its performance and that of the autistic adults. Adding noise to the control network did much less to improve its similarity to the control participants. This further suggest that sensory perception in autistic people may be the result of a so-called “noisy” brain.

Computational power

Looking forward, Kar sees several uses for computational models of visual processing. They can be further prodded, providing hypotheses that researchers might test in animal models. “I think facial emotion recognition is just the tip of the iceberg,” Kar says. They can also be used to select or even generate diagnostic content. Artificial intelligence could be used to generate content like movies and educational materials that optimally engages autistic children and adults. One might even tweak facial and other relevant pixels in what autistic people see in augmented reality goggles, work that Kar plans to pursue in the future.

Ultimately, Kar says, the work helps to validate the usefulness of computational models, especially image-processing neural networks. They formalize hypotheses and make them testable. Does one model or another better match behavioral data? “Even if these models are very far off from brains, they are falsifiable, rather than people just making up stories,” he says. “To me, that’s a more powerful version of science.”

Approaching human cognition from many angles

In January, as the Charles River was starting to freeze over, Keith Murray and the other members of MIT’s men’s heavyweight crew team took to erging on the indoor rowing machine. For 80 minutes at a time, Murray endured one of the most grueling workouts of his college experience. To distract himself from the pain, he would talk with his teammates, covering everything from great philosophical ideas to personal coffee preferences.

For Murray, virtually any conversation is an opportunity to explore how people think and why they think in certain ways. Currently a senior double majoring in computation and cognition, and linguistics and philosophy, Murray tries to understand the human experience based on knowledge from all of these fields.

“I’m trying to blend different approaches together to understand the complexities of human cognition,” he says. “For example, from a physiological perspective, the brain is just billions of neurons firing all at once, but this hardly scratches the surface of cognition.”

Murray grew up in Corydon, Indiana, where he attended the Indiana Academy for Science, Mathematics, and Humanities during his junior year of high school. He was exposed to philosophy there, learning the ideas of Plato, Socrates, and Thomas Aquinas, to name a few. When looking at colleges, Murray became interested in MIT because he wanted to learn about human thought processes from different perspectives. “Coming to MIT, I knew I wanted to do something philosophical. But I wanted to also be on the more technical side of things,” he says.

Once on campus, Murray immediately pursued an opportunity through the Undergraduate Research Opportunity Program (UROP) in the Digital Humanities Lab. There he worked with language-processing technology to analyze gendered language in various novels, with the end goal of displaying the data for an online audience. He learned about the basic mathematical models used for analyzing and presenting data online, to study the social implications of linguistic phrases and expressions.

Murray also joined the Concourse learning community, which brought together different perspectives from the humanities, sciences, and math in a weekly seminar. “I was exposed to some excellent examples of how to do interdisciplinary work,” he recalls.

In the summer before his sophomore year, Murray took a position as a researcher in the Harnett Lab, where instead of working with novels, he was working with mice. Alongside postdoc Lucas Fisher, Murray trained mice to do navigational tasks using virtual reality equipment. His goal was to explore neural encoding in navigation, understanding why the mice behaved in certain ways after being shown certain stimuli on the screens. Spending time in the lab, Murray became increasingly interested in neuroscience and the biological components behind human thought processes.

He sought out other neuroscience-related research experiences, which led him to explore a SuperUROP project in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). Working under Professor Nancy Lynch, he designed theoretical models of the retina using machine learning. Murray was excited to apply the techniques he learned in 9.40 (Introduction to Neural Computation) to address complex neurological problems. Murray considers this one of his most challenging research experiences, as the experience was entirely online.

“It was during the pandemic, so I had to learn a lot on my own; I couldn’t exactly do research in a lab. It was a big challenge, but at the end, I learned a lot and ended up getting a publication out of it,” he reflects.

This past semester, Murray has worked in the lab of Professor Ila Fiete in the McGovern Institute for Brain Research, constructing deep-learning models of animals performing navigational tasks. Through this UROP, which builds on his final project from Fiete’s class 9.49 (Neural Circuits for Cognition), Murray has been working to incorporate existing theoretical models of the hippocampus to investigate the intersection between artificial intelligence and neuroscience.

Reflecting on his varied research experiences, Murray says they have shown him new ways to explore the human brain from multiple perspectives, something he finds helpful as he tries to understand the complexity of human behavior.

Outside of his academic pursuits, Murray has continued to row with the crew team, where he walked on his first year. He sees rowing as a way to build up his strength, both physically and mentally. “When I’m doing my class work or I’m thinking about projects, I am using the same mental toughness that I developed during rowing,” he says. “That’s something I learned at MIT, to cultivate the dedication you put toward something. It’s all the same mental toughness whether you apply it to physical activities like rowing, or research projects.”

Looking ahead, Murray hopes to pursue a PhD in neuroscience, looking to find ways to incorporate his love of philosophy and human thought into his cognitive research. “I think there’s a lot more to do with neuroscience, especially with artificial intelligence. There are so many new technological developments happening right now,” he says.

What words can convey

From search engines to voice assistants, computers are getting better at understanding what we mean. That’s thanks to language processing programs that make sense of a staggering number of words, without ever being told explicitly what those words mean. Such programs infer meaning instead through statistics—and a new study reveals that this computational approach can assign many kinds of information to a single word, just like the human brain.

The study, published April 14, 2022, in the journal Nature Human Behavior, was co-led by Gabriel Grand, a graduate student at MIT’s Computer Science and Artificial Intelligence Laboratory, and Idan Blank, an assistant professor at the University of California, Los Angeles, and supervised by McGovern Investigator Ev Fedorenko, a cognitive neuroscientist who studies how the human brain uses and understands language, and Francisco Pereira at the National Institute of Mental Health. Fedorenko says the rich knowledge her team was able to find within computational language models demonstrates just how much can be learned about the world through language alone.

Early language models

The research team began its analysis of statistics-based language processing models in 2015, when the approach was new. Such models derive meaning by analyzing how often pairs of words co-occur in texts and using those relationships to assess the similarities of words’ meanings. For example, such a program might conclude that “bread” and “apple” are more similar to one another than they are to “notebook,” because “bread” and “apple” are often found in proximity to words like “eat” or “snack,” whereas “notebook” is not.

The models were clearly good at measuring words’ overall similarity to one another. But most words carry many kinds of information, and their similarities depend on which qualities are being evaluated. “Humans can come up with all these different mental scales to help organize their understanding of words,” explains Grand, a former undergraduate researcher in the Fedorenko lab. For examples, he says, “dolphins and alligators might be similar in size, but one is much more dangerous than the other.”

Grand and Idan Blank, who was then a graduate student at the McGovern Institute, wanted to know whether the models captured that same nuance. And if they did, how was the information organized?

To learn how the information in such a model stacked up to humans’ understanding of words, the team first asked human volunteers to score words along many different scales: Were the concepts those words conveyed big or small, safe or dangerous, wet or dry? Then, having mapped where people position different words along these scales, they looked to see whether language processing models did the same.

Grand explains that distributional semantic models use co-occurrence statistics to organize words into a huge, multidimensional matrix. The more similar words are to one another, the closer they are within that space. The dimensions of the space are vast, and there is no inherent meaning built into its structure. “In these word embeddings, there are hundreds of dimensions, and we have no idea what any dimension means,” he says. “We’re really trying to peer into this black box and say, ‘is there structure in here?’”

Word-vectors in the category ‘animals’ (blue circles) are orthogonally projected (light-blue lines) onto the feature subspace for ‘size’ (red line), defined as the vector difference between large−→−− and small−→−− (red circles). The three dimensions in this figure are arbitrary and were chosen via principal component analysis to enhance visualization (the original GloVe word embedding has 300 dimensions, and projection happens in that space). Image: Fedorenko lab

Specifically, they asked whether the semantic scales they had asked their volunteers use were represented in the model. So they looked to see where words in the space lined up along vectors defined by the extremes of those scales. Where did dolphins and tigers fall on line from “big” to “small,” for example? And were they closer together along that line than they were on a line representing danger (“safe” to “dangerous”)?

Across more than 50 sets of world categories and semantic scales, they found that the model had organized words very much like the human volunteers. Dolphins and tigers were judged to be similar in terms of size, but far apart on scales measuring danger or wetness. The model had organized the words in a way that represented many kinds of meaning—and it had done so based entirely on the words’ co-occurrences.

That, Fedorenko says, tells us something about the power of language. “The fact that we can recover so much of this rich semantic information from just these simple word co-occurrence statistics suggests that this is one very powerful source of learning about things that you may not even have direct perceptual experience with.”

Three from MIT awarded 2022 Paul and Daisy Soros Fellowships for New Americans

MIT graduate student Fernanda De La Torre, alumna Trang Luu ’18, SM ’20, and senior Syamantak Payra are recipients of the 2022 Paul and Daisy Soros Fellowships for New Americans.

De La Torre, Luu, and Payra are among 30 New Americans selected from a pool of over 1,800 applicants. The fellowship honors the contributions of immigrants and children of immigrants by providing $90,000 in funding for graduate school.

Students interested in applying to the P.D. Soros Fellowship for future years may contact Kim Benard, associate dean of distinguished fellowships in Career Advising and Professional Development.

Fernanda De La Torre

Fernanda De La Torre is a PhD student in the Department of Brain and Cognitive Sciences. With Professor Josh McDermott, she studies how we integrate vision and sound, and with Professor Robert Yang, she develops computational models of imagination.

De La Torre spent her early childhood with her younger sister and grandmother in Guadalajara, Mexico. At age 12, she crossed the Mexican border to reunite with her mother in Kansas City, Missouri. Shortly after, an abusive home environment forced De La Torre to leave her family and support herself throughout her early teens.

Despite her difficult circumstances, De La Torre excelled academically in high school. By winning various scholarships that would discretely take applications from undocumented students, she was able to continue her studies in computer science and mathematics at Kansas State University. There, she became intrigued by the mysteries of the human mind. During college, De La Torre received invaluable mentorship from her former high school principal, Thomas Herrera, who helped her become documented through the Violence Against Women Act. Her college professor, William Hsu, supported her interests in artificial intelligence and encouraged her to pursue a scientific career.

After her undergraduate studies, De La Torre won a post-baccalaureate fellowship from the Department of Brain and Cognitive Sciences at MIT, where she worked with Professor Tomaso Poggio on the theory of deep learning. She then transitioned into the department’s PhD program. Beyond contributing to scientific knowledge, De La Torre plans to use science to create spaces where all people, including those from backgrounds like her own, can innovate and thrive.

She says: “Immigrants face many obstacles, but overcoming them gives us a unique strength: We learn to become resilient, while relying on friends and mentors. These experiences foster both the desire and the ability to pay it forward to our community.”

Trang Luu

Trang Luu graduated from MIT with a BS in mechanical engineering in 2018, and a master of engineering degree in 2020. Her Soros award will support her graduate studies at Harvard University in the MBA/MS engineering sciences program.

Born in Saigon, Vietnam, Luu was 3 when her family immigrated to Houston, Texas. Watching her parents’ efforts to make a living in a land where they did not understand the culture or speak the language well, Luu wanted to alleviate hardship for her family. She took full responsibility for her education and found mentors to help her navigate the American education system. At home, she assisted her family in making and repairing household items, which fueled her excitement for engineering.

As an MIT undergraduate, Luu focused on assistive technology projects, applying her engineering background to solve problems impeding daily living. These projects included a new adaptive socket liner for below-the-knee amputees in Kenya, Ethiopia, and Thailand; a walking stick adapter for wheelchairs; a computer head pointer for patients with limited arm mobility, a safer makeshift cook stove design for street vendors in South Africa; and a quicker method to test new drip irrigation designs. As a graduate student in MIT D-Lab under the direction of Professor Daniel Frey, Luu was awarded a National Science Foundation Graduate Research Fellowship. In her graduate studies, Luu researched methods to improve evaporative cooling devices for off-grid farmers to reduce rapid fruit and vegetable deterioration.

These projects strengthened Luu’s commitment to innovating new technology and devices for people struggling with basic daily tasks. During her senior year, Luu collaborated on developing a working prototype of a wearable device that noninvasively reduces hand tremors associated with Parkinson’s disease or essential tremor. Observing patients’ joy after their tremors stopped compelled Luu and three co-founders to continue developing the device after college. Four years later, Encora Therapeutics has accomplished major milestones, including Breakthrough Device designation by the U.S. Food and Drug Administration.

Syamantak Payra

Hailing from Houston, Texas, Syamantak Payra is a senior majoring in electrical engineering and computer science, with minors in public policy and entrepreneurship and innovation. He will be pursuing a PhD in engineering at Stanford University, with the goal of creating new biomedical devices that can help improve daily life for patients worldwide and enhance health care outcomes for decades to come.

Payra’s parents had emigrated from India, and he grew up immersed in his grandparents’ rich Bengali culture. As a high school student, he conducted projects with NASA engineers at Johnson Space Center, experimented at home with his scientist parents, and competed in spelling bees and science fairs across the United States. Through these avenues and activities, Syamantak not only gained perspectives on bridging gaps between people, but also found passions for language, scientific discovery, and teaching others.

After watching his grandmother struggle with asthma and chronic obstructive pulmonary disease and losing his baby brother to brain cancer, Payra devoted himself to trying to use technology to solve health-care challenges. Payra’s proudest accomplishments include building a robotic leg brace for his paralyzed teacher and conducting free literacy workshops and STEM outreach programs that reached nearly a thousand underprivileged students across the Greater Houston Area.

At MIT, Payra has worked in Professor Yoel Fink’s research laboratory, creating digital sensor fibers that have been woven into intelligent garments that can assist in diagnosing illnesses, and in Professor Joseph Paradiso’s research laboratory, where he contributed to next-generation spacesuit prototypes that better protect astronauts on spacewalks. Payra’s research has been published by multiple scientific journals, and he was inducted into the National Gallery of America’s Young Inventors.

An optimized solution for face recognition

The human brain seems to care a lot about faces. It’s dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency—and neuroscientists at MIT’s McGovern Institute have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out.

The finding, reported March 16, 2022, in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition.

“The human brain’s solution is to segregate the processing of faces from the processing of objects,” explains Katharina Dobs, who led the study as a postdoctoral researcher in McGovern investigator Nancy Kanwisher’s lab. The artificial network that she trained did the same. “And that’s the same solution that we hypothesize any system that’s trained to recognize faces and to categorize objects would find,” she adds.

“These two completely different systems have figured out what a—if not the—good solution is. And that feels very profound,” says Kanwisher.

Functionally specific brain regions

More than twenty years ago, Kanwisher’s team discovered a small spot in the brain’s temporal lobe that responds specifically to faces. This region, which they named the fusiform face area, is one of many brain regions Kanwisher and others have found that are dedicated to specific tasks, such as the detection of written words, the perception of vocal songs, and understanding language.

Kanwisher says that as she has explored how the human brain is organized, she has always been curious about the reasons for that organization. Does the brain really need special machinery for facial recognition and other functions? “‘Why questions’ are very difficult in science,” she says. But with a sophisticated type of machine learning called a deep neural network, her team could at least find out how a different system would handle a similar task.

Dobs, who is now a research group leader at Justus Liebig University Giessen in Germany, assembled hundreds of thousands of images with which to train a deep neural network in face and object recognition. The collection included the faces of more than 1,700 different people and hundreds of different kinds of objects, from chairs to cheeseburgers. All of these were presented to the network, with no clues about which was which. “We never told the system that some of those are faces, and some of those are objects. So it’s basically just one big task,” Dobs says. “It needs to recognize a face identity, as well as a bike or a pen.”

Visualization of the preferred stimulus for example face-ranked filters. While filters in early layers (e.g., Conv5) were maximally activated by simple features, filters responded to features that appear somewhat like face parts (e.g., nose and eyes) in mid-level layers (e.g., Conv9) and appear to represent faces in a more holistic manner in late convolutional layers. Image: Kanwisher lab

As the program learned to identify the objects and faces, it organized itself into an information-processing network with that included units specifically dedicated to face recognition. Like the brain, this specialization occurred during the later stages of image processing. In both the brain and the artificial network, early steps in facial recognition involve more general vision processing machinery, and final stages rely on face-dedicated components.

It’s not known how face-processing machinery arises in a developing brain, but based on their findings, Kanwisher and Dobs say networks don’t necessarily require an innate face-processing mechanism to acquire that specialization. “We didn’t build anything face-ish into our network,” Kanwisher says. “The networks managed to segregate themselves without being given a face-specific nudge.”

Kanwisher says it was thrilling seeing the deep neural network segregate itself into separate parts for face and object recognition. “That’s what we’ve been looking at in the brain for twenty-some years,” she says. “Why do we have a separate system for face recognition in the brain? This tells me it is because that is what an optimized solution looks like.”

Now, she is eager to use deep neural nets to ask similar questions about why other brain functions are organized the way they are. “We have a new way to ask why the brain is organized the way it is,” she says. “How much of the structure we see in human brains will arise spontaneously by training networks to do comparable tasks?”

School of Engineering welcomes new faculty

The School of Engineering is welcoming 17 new faculty members to its departments, institutes, labs, and centers. With research and teaching activities ranging from the development of robotics and machine learning technologies to modeling the impact of elevated carbon dioxide levels on vegetation, they are poised to make significant contributions in new directions across the school and to a wide range of research efforts around the Institute.

“I am delighted to welcome our wonderful new faculty,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Their impact as talented educators, researchers, collaborators, and mentors will be felt across the School of Engineering and beyond as they strengthen our engineering community.”

Among the new faculty members are four from the Department of Electrical Engineering and Computer Science (EECS), which jointly reports into the School of Engineering and the MIT Stephen A. Schwarzman College of Computing.

Iwnetim “Tim” Abate will join the Department of Materials Science and Engineering in July 2023. He is currently both a Miller and Presidential Postdoctoral Fellow at the University of California at Berkeley. He received his MS and PhD in materials science and engineering from Stanford University and BS in physics from Minnesota State University at Moorhead. He also has research experience in industry (IBM) and at national labs (Los Alamos and SLAC National Accelerator Laboratories). Utilizing computational and experimental approaches in tandem, his research program at MIT will focus on the intersection of material chemistry, electrochemistry, and condensed matter physics to develop solutions for climate change and smart agriculture, including next-generation battery and sensor devices. Abate is also a co-founder and president of a nonprofit organization, SciFro Inc., working on empowering the African youth and underrepresented minorities in the United States to solve local problems through scientific research and innovation. He will continue working on expanding the vision and impact of SciFro with the MIT community. Abate received the Dan Cubicciotti Award of the Electrochemical Society, the EDGE and DARE graduate fellowships, the United Technologies Research Center fellowship, the John Stevens Jr. Memorial Award and the Justice, Equity, Diversity and Inclusion Graduation Award from Stanford University. He will hold the Toyota Career Development Professorship at MIT.

Kaitlyn Becker will join the Department of Mechanical Engineering as an assistant professor in August 2022. Becker received her PhD in materials science and mechanical engineering from Harvard University in 2021 and previously worked in industry as a manufacturing engineer at Cameron Health and a senior engineer for Nano Terra, Inc. She is a postdoc at the Harvard University School of Engineering and Applied Sciences and is also currently a senior glassblowing instructor in the Department of Materials Science and Engineering at MIT. Becker works on adaptive soft robots for grasping and manipulation of delicate structures from the desktop to the deep sea. Her research focuses on novel soft robotic platforms, adding functionality through innovations at the intersection of design and fabrication. She has developed novel fabrication methodologies and mechanical programming methods for large integrated arrays of soft actuators capable of collective manipulation and locomotion, and demonstrated integration of microfluidic circuits to control arrays of multichannel, two-degrees-of-freedom soft actuators. Becker received the National Science Foundation Graduate Research Fellowship in 2015, the Microsoft Graduate Women’s Scholarship in 2015, the Winston Chen Graduate Fellowship in 2015, and the Courtlandt S. Gross Memorial Scholarship in 2014.

Brandon J. DeKosky joined the Department of Chemical Engineering as an assistant professor in a newly introduced joint faculty position between the department and the Ragon Institute of MGH, MIT, and Harvard in September 2021. He received his BS in chemical engineering from University of Kansas and his PhD in chemical engineering from the University of Texas at Austin. He then did postdoctoral research at the Vaccine Research Center of the National Institute of Infectious Diseases. In 2017, Brandon launched his independent academic career as an assistant professor at the University of Kansas in a joint position with the Department of Chemical Engineering and the Department of Pharmaceutical Chemistry. He was also a member of the bioengineering graduate program. His research program focuses on developing and applying a suite of new high-throughput experimental and computational platforms for molecular analysis of adaptive immune responses, to accelerate precision drug discovery. He has received several notable recognitions, which include receipt of the NIH K99 Path to Independence and NIH DP5 Early Independence awards, the Cellular and Molecular Bioengineering Rising Star Award from the Biomedical Engineering Society, and the Career Development Award from the Congressionally Directed Medical Research Program’s Peer Reviewed Cancer Research Program.

Mohsen Ghaffari will join the Department of Electrical Engineering and Computer Science in April 2022. He received his BS from the Sharif University of Technology, and his MS and PhD in EECS from MIT. His research focuses on distributed and parallel algorithms for large graphs. Ghaffari received the ACM Doctoral Dissertation Honorable Mention Award, the ACM-EATCS Principles of Distributed Computing Doctoral Dissertation Award, and the George M. Sprowls Award for Best Computer Science PhD thesis at MIT. Before coming to MIT, he was on the faculty at ETH Zurich, where he received a prestigious European Research Council Starting Grant.

Aristide Gumyusenge joined the Department of Materials Science and Engineering in January. He is currently a postdoc at Stanford University working with Professor Zhenan Bao and Professor Alberto Salleo. He received a BS in chemistry from Wofford College in 2015 and a PhD in chemistry from Purdue University in 2019. His research background and interests are in semiconducting polymers, their processing and characterization, and their unique role in the future of electronics. Particularly, he has tackled longstanding challenges in operation stability of semiconducting polymers under extreme heat and has pioneered high-temperature plastic electronics. He has been selected as a PMSE Future Faculty Scholar (2021), the GLAM Postdoctoral Fellow (2020-22), and the MRS Arthur Nowick and Graduate Student Gold Awardee (2019), among other recognitions. At MIT, he will lead the Laboratory of Organic Materials for Smart Electronics (OMSE Lab). Through polymer design, novel processing strategies, and large-area manufacturing of electronic devices, he is interested in relating molecular design to device performance, especially transistor devices able to mimic and interface with biological systems. He will hold the Merton C. Flemings Career Development Professorship.

Mina Konakovic Lukovic will join the Department of Electrical Engineering and Computer Science as an assistant professor in July 2022. She received her BS and MS from the University of Belgrade, Faculty of Mathematics. She earned her PhD in 2019 in the School of Computer and Communication Sciences at the Swiss Federal Institute of Technology Lausanne, advised by Professor Mark Pauly. Currently a Schmidt Science Postdoctoral Fellow in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), she has been mentored by Professor Wojciech Matusik. Her research focuses on computer graphics, computational fabrication, 3D geometry processing, and machine learning, including architectural geometry and the design of programmable materials. She received the ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention, the Eurographics PhD Award, and was recently awarded the 2021 SIAM Activity Group on Geometric Design Early Career Prize.

Darcy McRose will join the Department of Civil and Environmental Engineering as an assistant professor in August 2022. She completed a BS in Earth systems at Stanford and a PhD in geosciences at Princeton University. Darcy is currently conducting postdoctoral work at Caltech, where she is mentored by Professor Dianne Newman in the divisions of Biology and Biological Engineering and Geological and Planetary Sciences. Her research program focuses on microbe-environment interactions and their effects on biogeochemical cycles, and incorporates techniques ranging from microbial physiology and genetics to geochemistry. A particular emphasis for this work is the production and use of secondary metabolites and small molecules in soils and sediments. McRose received the Caltech BBE Division postdoctoral fellowship in 2019 and is currently a Simons Foundation Marine Microbial Ecology postdoctoral fellow as well as a L’Oréal USA for Women in Science fellow.

Qin (Maggie) Qi joined the Department of Chemical Engineering as an assistant professor in January 2022. She received two BS degrees in chemical engineering and in operations research from Cornell University, before moving on to Stanford for her PhD. She then took on a postdoc position at Harvard University School of Engineering and Applied Sciences and the Wyss Institute. Maggie’s proposed research includes combining extensive theoretical and computational work on predictive models that guide experimental design. She seeks to investigate particle-cell biomechanics and function for better targeted cell-based therapies. She also plans to design microphysiological systems that elucidate hydrodynamics in complex organs, including delivery of drugs to the eye, and to examine ionic liquids as complex fluids for biomaterial design. She aims to push the boundaries of fluid mechanics, transport phenomena, and soft matter for human health and to innovate precision health care solutions. Maggie received the T.S. Lo Graduate Fellowship and the Stanford Graduate Fellowship in Science and Engineering. Among her accomplishments, Maggie was a participant in the inaugural class of the MIT Rising Stars in ChemE Program in 2018.

Manish Raghavan will join the MIT Sloan School of Management and the Department of Electrical Engineering and Computer Science as an assistant professor in September 2022. He shares a joint appointment with the MIT Schwarzman College of Computing. He received a bachelor’s degree in electrical engineering and computer science from the University of California at Berkeley, and PhD from the Computer Science department at Cornell University. Prior to joining MIT, he was a postdoc at the Harvard Center for Research on Computation and Society. His research interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. He is also a member of Cornell’s Artificial Intelligence, Policy, and Practice initiative and Mechanism Design for Social Good.

Ritu Raman joined the Department of Mechanical Engineering as an assistant professor and Brit and Alex d’Arbeloff Career Development Chair in August 2021. Raman received her PhD in mechanical engineering from the University of Illinois at Urbana-Champaign as an NSF Graduate Research Fellow in 2016 and completed a postdoctoral fellowship with Professor Robert Langer at MIT, funded by a NASEM Ford Foundation Fellowship and a L’Oréal USA For Women in Science Fellowship. Raman’s lab designs adaptive living materials powered by assemblies of living cells for applications ranging from medicine to machines. Currently, she is focused on using biological materials and engineering tools to build living neuromuscular tissues. Her goal is to help restore mobility to those who have lost it after disease or trauma and to deploy biological actuators as functional components in machines. Raman published the book Biofrabrication with MIT Press in September 2021. She was in the MIT Technology Review “35 Innovators Under 35” 2019 class, the Forbes “30 Under 30” 2018 class, and has received numerous awards including being named a National Academy of Sciences Kavli Frontiers of Science Fellow in 2020 and receiving the Science and Sartorius Prize for Regenerative Medicine and Cell Therapy in 2019. Ritu has championed many initiatives to empower women in science, including being named an AAAS IF/THEN ambassador and founding the Women in Innovation and Stem Database at MIT (WISDM).

Nidhi Seethapathi joined the Department of Brain and Cognitive Sciences and the Department of Electrical Engineering and Computer Science in January 2022. She shares a joint appointment with the MIT Schwarzman College of Computing. She received a bachelor’s degree in mechanical engineering from Veermata Jijabai Technological Institute and a PhD from the Movement Lab at Ohio State University. Her research interests include building computational predictive models of human movement with applications to autonomous and robot-aided neuromotor rehabilitation. In her work, she uses a combination of tools and approaches from dynamics, control theory, and machine learning. During her PhD, she was a Schlumberger Foundation Faculty for the Future Fellow. She then worked as a postdoc in the Kording Lab at University of Pennsylvania, developing data-driven tools for autonomous neuromotor rehabilitation, in collaboration with the Rehabilitation Robotics Lab.

Vincent Sitzmann will join the Department of Electrical Engineering and Computer Science as an assistant professor in July 2022. He earned his BS from the Technical University of Munich in 2015, his MS from Stanford in 2017, and his PhD from Stanford in 2020. At MIT, he will be the principal investigator of the Scene Representation Group, where he will lead research at the intersection of machine learning, graphics, neural rendering, and computer vision to build algorithms that learn to reconstruct, understand, and interact with 3D environments from incomplete observations the way humans can. Currently, Vincent is a postdoc at the MIT Computer Science and Artificial Intelligence Laboratory with Josh Tenenbaum, Bill Freeman, and Fredo Durand. Along with multiple scholarships and fellowships, he has been recognized with the NeurIPS Honorable Mention: Outstanding New Directions in 2019.

Tess Smidt joined the Department of Electrical Engineering and Computer Science as an assistant professor in September 2021. She earned her SB in physics from MIT in 2012 and her PhD in physics from the University of California at Berkeley in 2018. She is the principal investigator of the Atomic Architects group at the Research Laboratory of Electronics, where she works at the intersection of physics, geometry, and machine learning to design algorithms that aid in the understanding and design of physical systems. Her research focuses on machine learning that incorporates physical and geometric constraints, with applications to materials design. Prior to joining the MIT EECS faculty, she was the 2018 Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory and a software engineering intern on the Google Accelerated Sciences team, where she developed Euclidean symmetry equivariant neural networks which naturally handle 3D geometry and geometric tensor data.

Loza Tadesse will join the Department of Mechanical Engineering as an assistant professor in July 2023. She received her PhD in bioengineering from Stanford University in 2021 and previously was a medical student at St. Paul Hospital Millennium Medical College in Ethiopia. She is currently a postdoc at the University of California at Berkeley. Tadesse’s past research combines Raman spectroscopy and machine learning to develop a rapid, all-optical, and label-free bacterial diagnostic and antibiotic susceptibility testing system that aims to circumvent the time-consuming culturing step in “gold standard” methods. She aims to establish a research program that develops next-generation point-of-care diagnostic devices using spectroscopy, optical, and machine learning tools for application in resource limited clinical settings such as developing nations, military sites, and space exploration. Tadesse has been listed as a 2022 Forbes “30 Under 30” in health care, received many awards including the Biomedical Engineering Society (BMES) Career Development Award, the Stanford DARE Fellowship and the Gates Foundation “Call to Action” $200,000 grant for SciFro Inc., an educational nonprofit in Ethiopia, which she co-founded.

César Terrer joined the Department of Civil and Environmental Engineering as an assistant professor in July 2021. He obtained his PhD in ecosystem ecology and climate change from Imperial College London, where he started working at the interface between experiments and models to better understand the effects of elevated carbon dioxide on vegetation. His research has advanced the understanding on the effects of carbon dioxide in terrestrial ecosystems, the role of soil nutrients in a climate change context, and plant-soil interactions. Synthesizing observational data from carbon dioxide experiments and satellites through meta-analysis and machine learning, César has found that microbial interactions between plants and soils play a major role in the carbon cycle at a global scale, affecting the speed of global warming.

Haruko Wainwright joined the Department of Nuclear Science and Engineering as an assistant professor in January 2021. She received her BEng in engineering physics from Kyoto University, Japan in 2003, her MS in nuclear engineering in 2006, her MA in statistics in 2010, and her PhD in nuclear engineering in 2010 from University of California at Berkeley. Before joining MIT, she was a staff scientist in the Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory and an adjunct professor in nuclear engineering at UC Berkeley. Her research focuses on environmental modeling and monitoring technologies, with a particular emphasis on nuclear waste and nuclear-related contamination. She has been developing Bayesian methods for multi-type multiscale data integration and model-data integration. She leads and co-leads multiple interdisciplinary projects, including the U.S. Department of Energy’s Advanced Long-term Environmental Monitoring Systems (ALTEMIS) project, and the Artificial Intelligence for Earth System Predictability (AI4ESP) initiative.

Martin Wainwright will join the Department of Electrical Engineering and Computer Science in July 2022. He received a bachelor’s degree in mathematics from University of Waterloo, Canada, and PhD in EECS from MIT. Prior to joining MIT, he was the Chancellor’s Professor at the University of California at Berkeley, with a joint appointment between the Department of Statistics and the Department of EECS. His research interests include high-dimensional statistics, statistical machine learning, information theory, and optimization theory. Among other awards, he has received the COPSS Presidents’ Award (2014) from the Joint Statistical Societies, the David Blackwell Lectureship (2017), and Medallion Lectureship (2013) from the Institute of Mathematical Statistics, and Best Paper awards from the IEEE Signal Processing Society and IEEE Information Theory Society. He was a Section Lecturer at the International Congress of Mathematicians in 2014.