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      Making genetic prediction models more inclusive

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      While any two human genomes are about 99.9 percent identical, genetic variation in the remaining 0.1 percent plays an important role in shaping human diversity, including a person’s risk for developing certain diseases.

      Measuring the cumulative effect of these small genetic differences can provide an estimate of an individual’s genetic risk for a particular disease or their likelihood of having a particular trait. However, the majority of models used to generate these “polygenic scores” are based on studies done in people of European descent, and do not accurately gauge the risk for people of non-European ancestry or people whose genomes contain a mixture of chromosome regions inherited from previously isolated populations, also known as admixed ancestry.

      In an effort to make these genetic scores more inclusive, MIT researchers have created a new model that takes into account genetic information from people from a wider diversity of genetic ancestries across the world. Using this model, they showed that they could increase the accuracy of genetics-based predictions for a variety of traits, especially for people from populations that have been traditionally underrepresented in genetic studies.

      “For people of African ancestry, our model proved to be about 60 percent more accurate on average,” says Manolis Kellis, a professor of computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Broad Institute of MIT and Harvard. “For people of admixed genetic backgrounds more broadly, who have been excluded from most previous models, the accuracy of our model increased by an average of about 18 percent.”

      The researchers hope their more inclusive modeling approach could help improve health outcomes for a wider range of people and promote health equity by spreading the benefits of genomic sequencing more widely across the globe.

      “What we have done is created a method that allows you to be much more accurate for admixed and ancestry-diverse individuals, and ensure the results and the benefits of human genetics research are equally shared by everyone,” says MIT postdoc Yosuke Tanigawa, the lead and co-corresponding author of the paper, which appears today in open-access form in the American Journal of Human Genetics. The researchers have made all of their data publicly available for the broader scientific community to use.

      More inclusive models

      The work builds on the Human Genome Project, which mapped all of the genes found in the human genome, and on subsequent large-scale, cohort-based studies of how genetic variants in the human genome are linked to disease risk and other differences between individuals.

      These studies showed that the effect of any individual genetic variant on its own is typically very small. Together, these small effects add up and influence the risk of developing heart disease or diabetes, having a stroke, or being diagnosed with psychiatric disorders such as schizophrenia.

      “We have hundreds of thousands of genetic variants that are associated with complex traits, each of which is individually playing a weak effect, but together they are beginning to be predictive for disease predispositions,” Kellis says.

      However, most of these genome-wide association studies included few people of non-European descent, so polygenic risk models based on them translate poorly to non-European populations. People from different geographic areas can have different patterns of genetic variation, shaped by stochastic drift, population history, and environmental factors — for example, in people of African descent, genetic variants that protect against malaria are more common than in other populations. Those variants also affect other traits involving the immune system, such as counts of neutrophils, a type of immune cell. That variation would not be well-captured in a model based on genetic analysis of people of European ancestry alone.

      “If you are an individual of African descent, of Latin American descent, of Asian descent, then you are currently being left out by the system,” Kellis says. “This inequity in the utilization of genetic information for predicting risk of patients can cause unnecessary burden, unnecessary deaths, and unnecessary lack of prevention, and that’s where our work comes in.”

      Some researchers have begun trying to address these disparities by creating distinct models for people of European descent, of African descent, or of Asian descent. These emerging approaches assign individuals to distinct genetic ancestry groups, aggregate the data to create an association summary, and make genetic prediction models. However, these approaches still don’t represent people of admixed genetic backgrounds well.

      “Our approach builds on the previous work without requiring researchers to assign individuals or local genomic segments of individuals to predefined distinct genetic ancestry groups,” Tanigawa says. “Instead, we develop a single model for everybody by directly working on individuals across the continuum of their genetic ancestries.”

      In creating their new model, the MIT team used computational and statistical techniques that enabled them to study each individual’s unique genetic profile instead of grouping individuals by population. This methodological advancement allowed the researchers to include people of admixed ancestry, who made up nearly 10 percent of the UK Biobank dataset used for this study and currently account for about one in seven newborns in the United States.

      “Because we work at the individual level, there is no need for computing summary-level data for different populations,” Kellis says. “Thus, we did not need to exclude individuals of admixed ancestry, increasing our power by including more individuals and representing contributions from all populations in our combined model.”

      Better predictions

      To create their new model, the researchers used genetic data from more than 280,000 people, which was collected by UK Biobank, a large-scale biomedical database and research resource containing de-identified genetic, lifestyle, and health information from half a million U.K. participants. Using another set of about 81,000 held-out individuals from the UK Biobank, the researchers evaluated their model across 60 traits, which included traits related to body size and shape, such as height and body mass index, as well as blood traits such as white blood cell count and red blood cell count, which also have a genetic basis.

      The researchers found that, compared to models trained only on European-ancestry individuals, their model’s predictions are more accurate for all genetic ancestry groups. The most notable gain was for people of African ancestry, who showed 61 percent average improvements, even though they only made up about 1.5 percent of samples in UK Biobank. The researchers also saw improvements of 11 percent for people of South Asian descent and 5 percent for white British people. Predictions for people of admixed ancestry improved by about 18 percent.

      “When you bring all the individuals together in the training set, everybody contributes to the training of the polygenic score modeling on equal footing,” Tanigawa says. “Combined with increasingly more inclusive data collection efforts, our method can help leverage these efforts to improve predictive accuracy for all.”

      The MIT team hopes its approach can eventually be incorporated into tests of an individual’s risk of a variety of diseases. Such tests could be combined with conventional risk factors and used to help doctors diagnose disease or to help people manage their risk for certain diseases before they develop.

      “Our work highlights the power of diversity, equity, and inclusion efforts in the context of genomics research,” Tanigawa says.

      The researchers now hope to add even more data to their model, including data from the United States, and to apply it to additional traits that they didn’t analyze in this study.

      “This is just the start,” Kellis says. “We can’t wait to see more people join our effort to propel inclusive human genetics research.”

      The research was funded by the National Institutes of Health. More

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      To excel at engineering design, generative AI must learn to innovate, study finds

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      ChatGPT and other deep generative models are proving to be uncanny mimics. These AI supermodels can churn out poems, finish symphonies, and create new videos and images by automatically learning from millions of examples of previous works. These enormously powerful and versatile tools excel at generating new content that resembles everything they’ve seen before.

      But as MIT engineers say in a new study, similarity isn’t enough if you want to truly innovate in engineering tasks.

      “Deep generative models (DGMs) are very promising, but also inherently flawed,” says study author Lyle Regenwetter, a mechanical engineering graduate student at MIT. “The objective of these models is to mimic a dataset. But as engineers and designers, we often don’t want to create a design that’s already out there.”

      He and his colleagues make the case that if mechanical engineers want help from AI to generate novel ideas and designs, they will have to first refocus those models beyond “statistical similarity.”

      “The performance of a lot of these models is explicitly tied to how statistically similar a generated sample is to what the model has already seen,” says co-author Faez Ahmed, assistant professor of mechanical engineering at MIT. “But in design, being different could be important if you want to innovate.”

      In their study, Ahmed and Regenwetter reveal the pitfalls of deep generative models when they are tasked with solving engineering design problems. In a case study of bicycle frame design, the team shows that these models end up generating new frames that mimic previous designs but falter on engineering performance and requirements.

      When the researchers presented the same bicycle frame problem to DGMs that they specifically designed with engineering-focused objectives, rather than only statistical similarity, these models produced more innovative, higher-performing frames.

      The team’s results show that similarity-focused AI models don’t quite translate when applied to engineering problems. But, as the researchers also highlight in their study, with some careful planning of task-appropriate metrics, AI models could be an effective design “co-pilot.”

      “This is about how AI can help engineers be better and faster at creating innovative products,” Ahmed says. “To do that, we have to first understand the requirements. This is one step in that direction.”

      The team’s new study appeared recently online, and will be in the December print edition of the journal Computer Aided Design. The research is a collaboration between computer scientists at MIT-IBM Watson AI Lab and mechanical engineers in MIT’s DeCoDe Lab. The study’s co-authors include Akash Srivastava and Dan Gutreund at the MIT-IBM Watson AI Lab.

      Framing a problem

      As Ahmed and Regenwetter write, DGMs are “powerful learners, boasting unparalleled ability” to process huge amounts of data. DGM is a broad term for any machine-learning model that is trained to learn distribution of data and then use that to generate new, statistically similar content. The enormously popular ChatGPT is one type of deep generative model known as a large language model, or LLM, which incorporates natural language processing capabilities into the model to enable the app to generate realistic imagery and speech in response to conversational queries. Other popular models for image generation include DALL-E and Stable Diffusion.

      Because of their ability to learn from data and generate realistic samples, DGMs have been increasingly applied in multiple engineering domains. Designers have used deep generative models to draft new aircraft frames, metamaterial designs, and optimal geometries for bridges and cars. But for the most part, the models have mimicked existing designs, without improving the performance on existing designs.

      “Designers who are working with DGMs are sort of missing this cherry on top, which is adjusting the model’s training objective to focus on the design requirements,” Regenwetter says. “So, people end up generating designs that are very similar to the dataset.”

      In the new study, he outlines the main pitfalls in applying DGMs to engineering tasks, and shows that the fundamental objective of standard DGMs does not take into account specific design requirements. To illustrate this, the team invokes a simple case of bicycle frame design and demonstrates that problems can crop up as early as the initial learning phase. As a model learns from thousands of existing bike frames of various sizes and shapes, it might consider two frames of similar dimensions to have similar performance, when in fact a small disconnect in one frame — too small to register as a significant difference in statistical similarity metrics — makes the frame much weaker than the other, visually similar frame.

      Beyond “vanilla”
      An animation depicting transformations across common bicycle designs. Credit: Courtesy of the researchers

      The researchers carried the bicycle example forward to see what designs a DGM would actually generate after having learned from existing designs. They first tested a conventional “vanilla” generative adversarial network, or GAN — a model that has widely been used in image and text synthesis, and is tuned simply to generate statistically similar content. They trained the model on a dataset of thousands of bicycle frames, including commercially manufactured designs and less conventional, one-off frames designed by hobbyists.

      Once the model learned from the data, the researchers asked it to generate hundreds of new bike frames. The model produced realistic designs that resembled existing frames. But none of the designs showed significant improvement in performance, and some were even a bit inferior, with heavier, less structurally sound frames.

      The team then carried out the same test with two other DGMs that were specifically designed for engineering tasks. The first model is one that Ahmed previously developed to generate high-performing airfoil designs. He built this model to prioritize statistical similarity as well as functional performance. When applied to the bike frame task, this model generated realistic designs that also were lighter and stronger than existing designs. But it also produced physically “invalid” frames, with components that didn’t quite fit or overlapped in physically impossible ways.

      “We saw designs that were significantly better than the dataset, but also designs that were geometrically incompatible because the model wasn’t focused on meeting design constraints,” Regenwetter says.

      The last model the team tested was one that Regenwetter built to generate new geometric structures. This model was designed with the same priorities as the previous models, with the added ingredient of design constraints, and prioritizing physically viable frames, for instance, with no disconnections or overlapping bars. This last model produced the highest-performing designs, that were also physically feasible.

      “We found that when a model goes beyond statistical similarity, it can come up with designs that are better than the ones that are already out there,” Ahmed says. “It’s a proof of what AI can do, if it is explicitly trained on a design task.”

      For instance, if DGMs can be built with other priorities, such as performance, design constraints, and novelty, Ahmed foresees “numerous engineering fields, such as molecular design and civil infrastructure, would greatly benefit. By shedding light on the potential pitfalls of relying solely on statistical similarity, we hope to inspire new pathways and strategies in generative AI applications outside multimedia.” More

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      A new way to integrate data with physical objects

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      To get a sense of what StructCode is all about, says Mustafa Doğa Doğan, think of Superman. Not the “faster than a speeding bullet” and “more powerful than a locomotive” version, but a Superman, or Superwoman, who sees the world differently from ordinary mortals — someone who can look around a room and glean all kinds of information about ordinary objects that is not apparent to people with less penetrating faculties.

      That, in a nutshell, is “the high-level idea behind StructCode,” explains Doğan, a PhD student in electrical engineering and computer science at MIT and an affiliate of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). “The goal is to change the way we interact with objects” — to make those interactions more meaningful and more meaning-laden — “by embedding information into objects in ways that can be readily accessed.”

      StructCode grew out of an effort called InfraredTags, which Doğan and other colleagues introduced in 2022. That work, as well as the current project, was carried out in the laboratory of MIT Associate Professor Stefanie Mueller — Doğan’s advisor, who has taken part in both projects. In last year’s approach, “invisible” tags — that can only be seen with cameras capable of detecting infrared light — were used to reveal information about physical objects. The drawback there was that many cameras cannot perceive infrared light. Moreover, the method for fabricating these objects and affixing the tags to their surfaces relied on 3D printers, which tend to be very slow and often can only make objects that are small.

      StructCode, at least in its original version, relies on objects produced with laser-cutting techniques that can be manufactured within minutes, rather than the hours it might take on a 3D printer. Information can be extracted from these objects, moreover, with the RGB cameras that are commonly found in smartphones; the ability to operate in the infrared range of the spectrum is not required.

      In their initial demonstrations of the idea, the MIT-led team decided to construct their objects out of wood, making pieces such as furniture, picture frames, flowerpots, or toys that are well suited to laser-cut fabrication. A key question that had to be resolved was this: How can information be stored in a way that is unobtrusive and durable, as compared to externally-attached bar codes and QR codes, and also will not undermine an object’s structural integrity?

      The solution that the team has come up with, for now, is to rely on joints, which are ubiquitous in wooden objects made out of more than one component. Perhaps the most familiar is the finger joint, which has a kind of zigzag pattern whereby two wooden pieces adjoin at right angles such that every protruding “finger” along the joint of the first piece fits into a corresponding “gap” in the joint of the second piece and, similarly, every gap in the joint of the first piece is filled with a finger from the second.

      “Joints have these repeating features, which are like repeating bits,” Dogan says. To create a code, the researchers slightly vary the length of the gaps or fingers. A standard size length is accorded a 1. A slightly shorter length is assigned a 0, and a slightly longer length is assigned a 2. The encoding scheme is based on the sequence of these numbers, or bits, that can be observed along a joint. For every string of four bits, there are 81 (34) possible variations.

      The team also demonstrated ways of encoding messages in “living hinges” — a kind of joint that is made by taking a flat, rigid piece of material and making it bendable by cutting a series of parallel, vertical lines. As with the finger joints, the distance between these lines can be varied: 1 being the standard length, 0 being a slightly shorter length, and 2 being slightly longer. And in this way, a code can be assembled from an object that contains a living hinge.

      The idea is described in a paper, “StructCode: Leveraging Fabrication Artifacts to Store Data in Laser-Cut Objects,” that was presented this month at the 2023 ACM Symposium on Computational Fabrication in New York City. Doğan, the paper’s first author, is joined by Mueller and four coauthors — recent MIT alumna Grace Tang ’23, MNG ’23; MIT undergraduate Richard Qi; University of California at Berkeley graduate student Vivian Hsinyueh Chan; and Cornell University Assistant Professor Thijs Roumen.

      “In the realm of materials and design, there is often an inclination to associate novelty and innovation with entirely new materials or manufacturing techniques,” notes Elvin Karana, a professor of materials innovation and design at the Delft University of Technology. One of the things that impresses Karana most about StructCode is that it provides a novel means of storing data by “applying a commonly used technique like laser cutting and a material as ubiquitous as wood.”

      The idea for StructCode, adds University of Colorado computer scientist Ellen Yi-Luen Do, “is “simple, elegant, and totally makes sense. It’s like having the Rosetta Stone to help decipher Egyptian hieroglyphs.”

      Patrick Baudisch, a computer scientist at the Hasso Plattner Institute in Germany, views StructCode as “a great step forward for personal fabrication. It takes a key piece of functionality that’s only offered today for mass-produced goods and brings it to custom objects.”

      Here, in brief, is how it works: First, a laser cutter — guided by a model created via StructCode — fabricates an object into which encoded information has been embedded. After downloading a StructCode app, an user can decode the hidden message by pointing a cellphone camera at the object, which can (aided by StructCode software) detect subtle variations in length found in an object’s outward-facing joints or living hinges.

      The process is even easier if the user is equipped with augmented reality glasses, Doğan says. “In that case, you don’t need to point a camera. The information comes up automatically.” And that can give people more of the “superpowers” that the designers of StructCode hope to confer.

      “The object doesn’t need to contain a lot of information,” Doğan adds. “Just enough — in the form of, say, URLs — to direct people to places they can find out what they need to know.”

      Users might be sent to a website where they can obtain information about the object — how to care for it, and perhaps eventually how to disassemble it and recycle (or safely dispose of) its contents. A flowerpot that was made with living hinges might inform a user, based on records that are maintained online, as to when the plant inside the pot was last watered and when it needs to be watered again. Children examining a toy crocodile could, through StructCode, learn scientific details about various parts of the animal’s anatomy. A picture frame made with finger joints modified by StructCode could help people find out about the painting inside the frame and about the person (or persons) who created the artwork — perhaps linking to a video of an artist talking about this work directly.

      “This technique could pave the way for new applications, such as interactive museum exhibits,” says Raf Ramakers, a computer scientist at Hasselt University in Belgium. “It holds the potential for broadening the scope of how we perceive and interact with everyday objects” — which is precisely the goal that motivates the work of Doğan and his colleagues.

      But StructCode is not the end of the line, as far as Doğan and his collaborators are concerned. The same general approach could be adapted to other manufacturing techniques besides laser cutting, and information storage doesn’t have to be confined to the joints of wooden objects. Data could be represented, for instance, in the texture of leather, within the pattern of woven or knitted pieces, or concealed by other means within an image. Doğan is excited by the breadth of available options and by the fact that their “explorations into this new realm of possibilities, designed to make objects and our world more interactive, are just beginning.” More

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      AI copilot enhances human precision for safer aviation

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      Imagine you’re in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they’re always looking out for different things. If they’re both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes over.

      Meet the Air-Guardian, a system developed by researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). As modern pilots grapple with an onslaught of information from multiple monitors, especially during critical moments, Air-Guardian acts as a proactive copilot; a partnership between human and machine, rooted in understanding attention.

      But how does it determine attention, exactly? For humans, it uses eye-tracking, and for the neural system, it relies on something called “saliency maps,” which pinpoint where attention is directed. The maps serve as visual guides highlighting key regions within an image, aiding in grasping and deciphering the behavior of intricate algorithms. Air-Guardian identifies early signs of potential risks through these attention markers, instead of only intervening during safety breaches like traditional autopilot systems. 

      The broader implications of this system reach beyond aviation. Similar cooperative control mechanisms could one day be used in cars, drones, and a wider spectrum of robotics.

      “An exciting feature of our method is its differentiability,” says MIT CSAIL postdoc Lianhao Yin, a lead author on a new paper about Air-Guardian. “Our cooperative layer and the entire end-to-end process can be trained. We specifically chose the causal continuous-depth neural network model because of its dynamic features in mapping attention. Another unique aspect is adaptability. The Air-Guardian system isn’t rigid; it can be adjusted based on the situation’s demands, ensuring a balanced partnership between human and machine.”

      In field tests, both the pilot and the system made decisions based on the same raw images when navigating to the target waypoint. Air-Guardian’s success was gauged based on the cumulative rewards earned during flight and shorter path to the waypoint. The guardian reduced the risk level of flights and increased the success rate of navigating to target points. 

      “This system represents the innovative approach of human-centric AI-enabled aviation,” adds Ramin Hasani, MIT CSAIL research affiliate and inventor of liquid neural networks. “Our use of liquid neural networks provides a dynamic, adaptive approach, ensuring that the AI doesn’t merely replace human judgment but complements it, leading to enhanced safety and collaboration in the skies.”

      The true strength of Air-Guardian is its foundational technology. Using an optimization-based cooperative layer using visual attention from humans and machine, and liquid closed-form continuous-time neural networks (CfC) known for its prowess in deciphering cause-and-effect relationships, it analyzes incoming images for vital information. Complementing this is the VisualBackProp algorithm, which identifies the system’s focal points within an image, ensuring clear understanding of its attention maps. 

      For future mass adoption, there’s a need to refine the human-machine interface. Feedback suggests an indicator, like a bar, might be more intuitive to signify when the guardian system takes control.

      Air-Guardian heralds a new age of safer skies, offering a reliable safety net for those moments when human attention wavers.

      “The Air-Guardian system highlights the synergy between human expertise and machine learning, furthering the objective of using machine learning to augment pilots in challenging scenarios and reduce operational errors,” says Daniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT, director of CSAIL, and senior author on the paper.”One of the most interesting outcomes of using a visual attention metric in this work is the potential for allowing earlier interventions and greater interpretability by human pilots,” says Stephanie Gil, assistant professor of computer science at Harvard University, who was not involved in the work. “This showcases a great example of how AI can be used to work with a human, lowering the barrier for achieving trust by using natural communication mechanisms between the human and the AI system.”

      This research was partially funded by the U.S. Air Force (USAF) Research Laboratory, the USAF Artificial Intelligence Accelerator, the Boeing Co., and the Office of Naval Research. The findings don’t necessarily reflect the views of the U.S. government or the USAF. More

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      Improving accessibility of online graphics for blind users

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      The beauty of a nice infographic published alongside a news or magazine story is that it makes numeric data more accessible to the average reader. But for blind and visually impaired users, such graphics often have the opposite effect.

      For visually impaired users — who frequently rely on screen-reading software that speaks words or numbers aloud as the user moves a cursor across the screen — a graphic may be nothing more than a few words of alt text, such as a chart’s title. For instance, a map of the United States displaying population rates by county might have alt text in the HTML that says simply, “A map of the United States with population rates by county.” The data has been buried in an image, making it entirely inaccessible.

      “Charts have these various visual features that, as a [sighted] reader, you can shift your attention around, look at high-level patterns, look at individual data points, and you can do this on the fly,” says Jonathan Zong, a 2022 MIT Morningside Academy for Design (MAD) Fellow and PhD student in computer science, who points out that even when a graphic includes alt text that interprets the data, the visually impaired user must accept the findings as presented.

      “If you’re [blind and] using a screen reader, the text description imposes a linear predefined reading order. So, you’re beholden to the decisions that the person who wrote the text made about what information was important to include.”

      While some graphics do include data tables that a screen reader can read, it requires the user to remember all the data from each row and column as they move on to the next one. According to the National Federation of the Blind, Zong says, there are 7 million people living in the United States with visual disabilities, and nearly 97 percent of top-level pages on the internet are not accessible to screen readers. The problem, he points out, is an especially difficult one for blind researchers to get around. Some researchers with visual impairments rely on a sighted collaborator to read and help interpret graphics in peer-reviewed research.

      Working with the Visualization Group at the Computer Science and Artificial Intelligence Lab (CSAIL) on a project led by Associate Professor Arvind Satyanarayan that includes Daniel Hajas, a blind researcher and innovation manager at the Global Disability Innovation Hub in England, Zong and others have written an open-source Javascript software program named Olli that solves this problem when it’s included on a website. Olli is able to go from big-picture analysis of a chart to the finest grain of detail to give the user the ability to select the degree of granularity that interests them.

      “We want to design richer screen-reader experiences for visualization with a hierarchical structure, multiple ways to navigate, and descriptions at varying levels of granularity to provide self-guided, open-ended exploration for the user.”

      Next steps with Olli are incorporating multi-sensory software to integrate text and visuals with sound, such as having a musical note that moves up or down the harmonic scale to indicate the direction of data on a linear graph, and possibly even developing tactile interpretations of data. Like most of the MAD Fellows, Zong integrates his science and engineering skills with design and art to create solutions to real-world problems affecting individuals. He’s been recognized for his work in both the visual arts and computer science. He holds undergraduate degrees in computer science and visual arts with a focus on graphic design from Princeton University, where his research was on the ethics of data collection.

      “The throughline is the idea that design can help us make progress on really tough social and ethical questions,” Zong says, calling software for accessible data visualization an “intellectually rich area for design.” “We’re thinking about ways to translate charts and graphs into text descriptions that can get read aloud as speech, or thinking about other kinds of audio mappings to sonify data, and we’re even exploring some tactile methods to understand data,” he says.

      “I get really excited about design when it’s a way to both create things that are useful to people in everyday life and also make progress on larger conversations about technology and society. I think working in accessibility is a great way to do that.”

      Another problem at the intersection of technology and society is the ethics of taking user data from social media for large-scale studies without the users’ awareness. While working as a summer graduate research fellow at Cornell’s Citizens and Technology Lab, Zong helped create an open-source software called Bartleby that can be used in large anonymous data research studies. After researchers collect data, but before analysis, Bartleby would automatically send an email message to every user whose data was included, alert them to that fact and offer them the choice to review the resulting data table and opt out of the study. Bartleby was honored in the student category of Fast Company’s Innovation by Design Awards for 2022. In November the same year, Forbes magazine named Jonathan Zong in its Forbes 30 Under 30 in Science 2023 list for his work in data visualization accessibility.

      The underlying theme to all Zong’s work is the exploration of autonomy and agency, even in his artwork, which is heavily inclusive of text and semiotic play. In “Public Display,” he created a handmade digital display font by erasing parts of celebrity faces that were taken from a facial recognition dataset. The piece was exhibited in 2020 in MIT’s Wiesner Gallery, and received the third-place prize in the MIT Schnitzer Prize in the Visual Arts that year. The work deals not only with the neurological aspects of distinguishing faces from typefaces, but also with the implications for erasing individuals’ identities through the practice of using facial recognition programs that often target individuals in communities of color in unfair ways. Another of his works, “Biometric Sans,” a typography system that stretches letters based on a person’s typing speed, will be included in a show at the Harvard Science Center sometime next fall.

      “MAD, particularly the large events MAD jointly hosted, played a really important function in showing the rest of MIT that this is the kind of work we value. This is what design can look like and is capable of doing. I think it all contributes to that culture shift where this kind of interdisciplinary work can be valued, recognized, and serve the public.

      “There are shared ideas around embodiment and representation that tie these different pursuits together for me,” Zong says. “In the ethics work, and the art on surveillance, I’m thinking about whether data collectors are representing people the way they want to be seen through data. And similarly, the accessibility work is about whether we can make systems that are flexible to the way people want to use them.” More

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      A more effective experimental design for engineering a cell into a new state

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      A strategy for cellular reprogramming involves using targeted genetic interventions to engineer a cell into a new state. The technique holds great promise in immunotherapy, for instance, where researchers could reprogram a patient’s T-cells so they are more potent cancer killers. Someday, the approach could also help identify life-saving cancer treatments or regenerative therapies that repair disease-ravaged organs.

      But the human body has about 20,000 genes, and a genetic perturbation could be on a combination of genes or on any of the over 1,000 transcription factors that regulate the genes. Because the search space is vast and genetic experiments are costly, scientists often struggle to find the ideal perturbation for their particular application.   

      Researchers from MIT and Harvard University developed a new, computational approach that can efficiently identify optimal genetic perturbations based on a much smaller number of experiments than traditional methods.

      Their algorithmic technique leverages the cause-and-effect relationship between factors in a complex system, such as genome regulation, to prioritize the best intervention in each round of sequential experiments.

      The researchers conducted a rigorous theoretical analysis to determine that their technique did, indeed, identify optimal interventions. With that theoretical framework in place, they applied the algorithms to real biological data designed to mimic a cellular reprogramming experiment. Their algorithms were the most efficient and effective.

      “Too often, large-scale experiments are designed empirically. A careful causal framework for sequential experimentation may allow identifying optimal interventions with fewer trials, thereby reducing experimental costs,” says co-senior author Caroline Uhler, a professor in the Department of Electrical Engineering and Computer Science (EECS) who is also co-director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems and Society (IDSS).

      Joining Uhler on the paper, which appears today in Nature Machine Intelligence, are lead author Jiaqi Zhang, a graduate student and Eric and Wendy Schmidt Center Fellow; co-senior author Themistoklis P. Sapsis, professor of mechanical and ocean engineering at MIT and a member of IDSS; and others at Harvard and MIT.

      Active learning

      When scientists try to design an effective intervention for a complex system, like in cellular reprogramming, they often perform experiments sequentially. Such settings are ideally suited for the use of a machine-learning approach called active learning. Data samples are collected and used to learn a model of the system that incorporates the knowledge gathered so far. From this model, an acquisition function is designed — an equation that evaluates all potential interventions and picks the best one to test in the next trial.

      This process is repeated until an optimal intervention is identified (or resources to fund subsequent experiments run out).

      “While there are several generic acquisition functions to sequentially design experiments, these are not effective for problems of such complexity, leading to very slow convergence,” Sapsis explains.

      Acquisition functions typically consider correlation between factors, such as which genes are co-expressed. But focusing only on correlation ignores the regulatory relationships or causal structure of the system. For instance, a genetic intervention can only affect the expression of downstream genes, but a correlation-based approach would not be able to distinguish between genes that are upstream or downstream.

      “You can learn some of this causal knowledge from the data and use that to design an intervention more efficiently,” Zhang explains.

      The MIT and Harvard researchers leveraged this underlying causal structure for their technique. First, they carefully constructed an algorithm so it can only learn models of the system that account for causal relationships.

      Then the researchers designed the acquisition function so it automatically evaluates interventions using information on these causal relationships. They crafted this function so it prioritizes the most informative interventions, meaning those most likely to lead to the optimal intervention in subsequent experiments.

      “By considering causal models instead of correlation-based models, we can already rule out certain interventions. Then, whenever you get new data, you can learn a more accurate causal model and thereby further shrink the space of interventions,” Uhler explains.

      This smaller search space, coupled with the acquisition function’s special focus on the most informative interventions, is what makes their approach so efficient.

      The researchers further improved their acquisition function using a technique known as output weighting, inspired by the study of extreme events in complex systems. This method carefully emphasizes interventions that are likely to be closer to the optimal intervention.

      “Essentially, we view an optimal intervention as an ‘extreme event’ within the space of all possible, suboptimal interventions and use some of the ideas we have developed for these problems,” Sapsis says.    

      Enhanced efficiency

      They tested their algorithms using real biological data in a simulated cellular reprogramming experiment. For this test, they sought a genetic perturbation that would result in a desired shift in average gene expression. Their acquisition functions consistently identified better interventions than baseline methods through every step in the multi-stage experiment.

      “If you cut the experiment off at any stage, ours would still be more efficient than the baselines. This means you could run fewer experiments and get the same or better results,” Zhang says.

      The researchers are currently working with experimentalists to apply their technique toward cellular reprogramming in the lab.

      Their approach could also be applied to problems outside genomics, such as identifying optimal prices for consumer products or enabling optimal feedback control in fluid mechanics applications.

      In the future, they plan to enhance their technique for optimizations beyond those that seek to match a desired mean. In addition, their method assumes that scientists already understand the causal relationships in their system, but future work could explore how to use AI to learn that information, as well.

      This work was funded, in part, by the Office of Naval Research, the MIT-IBM Watson AI Lab, the MIT J-Clinic for Machine Learning and Health, the Eric and Wendy Schmidt Center at the Broad Institute, a Simons Investigator Award, the Air Force Office of Scientific Research, and a National Science Foundation Graduate Fellowship. More

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      J-PAL North America and Results for America announce 18 collaborations with state and local governments

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      J-PAL North America and Results for America have announced 18 new partnerships with state and local governments across the country through their Leveraging Evidence and Evaluation for Equitable Recovery (LEVER) programming, which launched in April of this year. 

      As state and local leaders leverage federal relief funding to invest in their communities, J-PAL North America and Results for America are providing in-depth support to agencies in using data, evaluation, and evidence to advance effective and equitable government programming for generations to come. The 18 new collaborators span the contiguous United States and represent a wide range of pressing and innovative uses of federal Covid-19 recovery funding.

      These partnerships are a key component of the LEVER program, run by J-PAL North America — a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) — and Results for America — a nonprofit organization that helps government agencies harness the power of evidence and data. Through 2024, LEVER will continue to provide a suite of resources, training, and evaluation design services to prepare state and local government agencies to rigorously evaluate their own programs and to harness existing evidence in developing programs and policies using federal recovery dollars.

      J-PAL North America is working with four leading government agencies following a call for proposals to the LEVER Evaluation Incubator in June. These agencies will work with J-PAL staff to design randomized evaluations to understand the causal impact of important programs that contribute to their jurisdictions’ recovery from Covid-19.

      Connecticut’s Medicaid office, operating out of the state’s Department of Social Services, is working to improve vaccine access and awareness among youth. “Connecticut Medicaid is thrilled to work with J-PAL North America. The technical expertise and training that we receive will expand our knowledge during ‘testing and learning’ interventions that improve the health of our members,” says Gui Woolston, the director of Medicaid and Division of Health Services. 

      Athens-Clarke County Unified Government is invested in evaluating programming for youth development and violence prevention implemented by the Boys and Girls Club of Athens. Their goal is “to measure and transparently communicate program impact,” explains Paige Seago, the data and outcomes coordinator for the American Rescue Plan Act. “The ability to continually iterate and tailor programs to better meet community goals is crucial to long-term success.”

      The County of San Diego’s newly formed Office of Evaluation, Performance, and Analytics is evaluating a pilot program providing rental subsidies for older adults. “Randomized evaluation can help us understand if rent subsidies will help prevent seniors from becoming homeless and will give us useful information about how to move forward,” says Chief Evaluation Officer Ricardo Basurto-Dávila. 

      In King County, Washington, the Executive Climate Office is planning to evaluate efforts to increase equitable access to household energy efficiency programs. “Because of J-PAL’s support, we have confidence that we can reduce climate impacts and extend home electrification benefits to lower-income homeowners in King County — homeowners who otherwise may not have the ability to participate in the clean energy transition,” says King County Climate Director Marissa Aho.

      Fourteen additional state and local agencies are working with Results for America as part of the LEVER Training Sprint. Together, they will develop policies that catalyze sustainable evidence building within government. 

      Jurisdictions selected for the Training Sprint represent government leaders at the city, county, and state levels — all of whom are committed to creating an evaluation framework for policy that will prioritize evidence-based decision-making across the country. Over the course of 10 weeks, with access to tools and coaching, each team will develop an internal implementation policy by embedding key evaluation and evidence practices into their jurisdiction’s decision-making processes. Participants will finish the Training Sprint with a robust decision-making framework that translates their LEVER implementation policies into actionable planning guidance. 

      Government leaders will utilize the LEVER Training Sprint to build a culture of data and evidence focused on leveraging evaluation policies to invest in delivering tangible results for their residents. About their participation in the LEVER Training Sprint, Dana Williams from Denver, Colorado says, “Impact evaluation is such an integral piece to understanding the past, present, and future. I’m excited to participate in the LEVER Training Sprint to better inform and drive evidence-based programming in Denver.”

      The Training Sprint is a part of a growing movement to ground government innovation in data and evidence. Kermina Hanna from the State of New Jersey notes, “It’s vital that we cement a data-driven commitment to equity in government operations, and I’m really excited for this opportunity to develop a national network of colleagues in government who share this passion and dedication to responsive public service.”

      Jurisdictions selected for the Training Sprint are: 

      Boston, Massachusetts;
      Carlsbad, California;
      Connecticut;
      Dallas, Texas;
      Denver City/County, Colorado;
      Fort Collins, Colorado;
      Guilford County, North Carolina;
      King County, Washington;
      Long Beach, California;
      Los Angeles, California;
      New Jersey;
      New Mexico;
      Pittsburgh, Pennsylvania; and
      Washington County, Oregon.
      Those interested in learning more can fill out the LEVER intake form. Please direct any questions about the Evaluation Incubator to Louise Geraghty and questions about the Training Sprint to Chelsea Powell. More

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      From physics to generative AI: An AI model for advanced pattern generation

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      Generative AI, which is currently riding a crest of popular discourse, promises a world where the simple transforms into the complex — where a simple distribution evolves into intricate patterns of images, sounds, or text, rendering the artificial startlingly real. 

      The realms of imagination no longer remain as mere abstractions, as researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have brought an innovative AI model to life. Their new technology integrates two seemingly unrelated physical laws that underpin the best-performing generative models to date: diffusion, which typically illustrates the random motion of elements, like heat permeating a room or a gas expanding into space, and Poisson Flow, which draws on the principles governing the activity of electric charges.

      This harmonious blend has resulted in superior performance in generating new images, outpacing existing state-of-the-art models. Since its inception, the “Poisson Flow Generative Model ++” (PFGM++) has found potential applications in various fields, from antibody and RNA sequence generation to audio production and graph generation.

      The model can generate complex patterns, like creating realistic images or mimicking real-world processes. PFGM++ builds off of PFGM, the team’s work from the prior year. PFGM takes inspiration from the means behind the mathematical equation known as the “Poisson” equation, and then applies it to the data the model tries to learn from. To do this, the team used a clever trick: They added an extra dimension to their model’s “space,” kind of like going from a 2D sketch to a 3D model. This extra dimension gives more room for maneuvering, places the data in a larger context, and helps one approach the data from all directions when generating new samples. 

      “PFGM++ is an example of the kinds of AI advances that can be driven through interdisciplinary collaborations between physicists and computer scientists,” says Jesse Thaler, theoretical particle physicist in MIT’s Laboratory for Nuclear Science’s Center for Theoretical Physics and director of the National Science Foundation’s AI Institute for Artificial Intelligence and Fundamental Interactions (NSF AI IAIFI), who was not involved in the work. “In recent years, AI-based generative models have yielded numerous eye-popping results, from photorealistic images to lucid streams of text. Remarkably, some of the most powerful generative models are grounded in time-tested concepts from physics, such as symmetries and thermodynamics. PFGM++ takes a century-old idea from fundamental physics — that there might be extra dimensions of space-time — and turns it into a powerful and robust tool to generate synthetic but realistic datasets. I’m thrilled to see the myriad of ways ‘physics intelligence’ is transforming the field of artificial intelligence.”

      The underlying mechanism of PFGM isn’t as complex as it might sound. The researchers compared the data points to tiny electric charges placed on a flat plane in a dimensionally expanded world. These charges produce an “electric field,” with the charges looking to move upwards along the field lines into an extra dimension and consequently forming a uniform distribution on a vast imaginary hemisphere. The generation process is like rewinding a videotape: starting with a uniformly distributed set of charges on the hemisphere and tracking their journey back to the flat plane along the electric lines, they align to match the original data distribution. This intriguing process allows the neural model to learn the electric field, and generate new data that mirrors the original. 

      The PFGM++ model extends the electric field in PFGM to an intricate, higher-dimensional framework. When you keep expanding these dimensions, something unexpected happens — the model starts resembling another important class of models, the diffusion models. This work is all about finding the right balance. The PFGM and diffusion models sit at opposite ends of a spectrum: one is robust but complex to handle, the other simpler but less sturdy. The PFGM++ model offers a sweet spot, striking a balance between robustness and ease of use. This innovation paves the way for more efficient image and pattern generation, marking a significant step forward in technology. Along with adjustable dimensions, the researchers proposed a new training method that enables more efficient learning of the electric field. 

      To bring this theory to life, the team resolved a pair of differential equations detailing these charges’ motion within the electric field. They evaluated the performance using the Frechet Inception Distance (FID) score, a widely accepted metric that assesses the quality of images generated by the model in comparison to the real ones. PFGM++ further showcases a higher resistance to errors and robustness toward the step size in the differential equations.

      Looking ahead, they aim to refine certain aspects of the model, particularly in systematic ways to identify the “sweet spot” value of D tailored for specific data, architectures, and tasks by analyzing the behavior of estimation errors of neural networks. They also plan to apply the PFGM++ to the modern large-scale text-to-image/text-to-video generation.

      “Diffusion models have become a critical driving force behind the revolution in generative AI,” says Yang Song, research scientist at OpenAI. “PFGM++ presents a powerful generalization of diffusion models, allowing users to generate higher-quality images by improving the robustness of image generation against perturbations and learning errors. Furthermore, PFGM++ uncovers a surprising connection between electrostatics and diffusion models, providing new theoretical insights into diffusion model research.”

      “Poisson Flow Generative Models do not only rely on an elegant physics-inspired formulation based on electrostatics, but they also offer state-of-the-art generative modeling performance in practice,” says NVIDIA Senior Research Scientist Karsten Kreis, who was not involved in the work. “They even outperform the popular diffusion models, which currently dominate the literature. This makes them a very powerful generative modeling tool, and I envision their application in diverse areas, ranging from digital content creation to generative drug discovery. More generally, I believe that the exploration of further physics-inspired generative modeling frameworks holds great promise for the future and that Poisson Flow Generative Models are only the beginning.”

      Authors on a paper about this work include three MIT graduate students: Yilun Xu of the Department of Electrical Engineering and Computer Science (EECS) and CSAIL, Ziming Liu of the Department of Physics and the NSF AI IAIFI, and Shangyuan Tong of EECS and CSAIL, as well as Google Senior Research Scientist Yonglong Tian PhD ’23. MIT professors Max Tegmark and Tommi Jaakkola advised the research.

      The team was supported by the MIT-DSTA Singapore collaboration, the MIT-IBM Grand Challenge project, National Science Foundation grants, The Casey and Family Foundation, the Foundational Questions Institute, the Rothberg Family Fund for Cognitive Science, and the ML for Pharmaceutical Discovery and Synthesis Consortium. Their work was presented at the International Conference on Machine Learning this summer. More