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    How to assess a general-purpose AI model’s reliability before it’s deployed

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    Foundation models are massive deep-learning models that have been pretrained on an enormous amount of general-purpose, unlabeled data. They can be applied to a variety of tasks, like generating images or answering customer questions.But these models, which serve as the backbone for powerful artificial intelligence tools like ChatGPT and DALL-E, can offer up incorrect or misleading information. In a safety-critical situation, such as a pedestrian approaching a self-driving car, these mistakes could have serious consequences.To help prevent such mistakes, researchers from MIT and the MIT-IBM Watson AI Lab developed a technique to estimate the reliability of foundation models before they are deployed to a specific task.They do this by considering a set of foundation models that are slightly different from one another. Then they use their algorithm to assess the consistency of the representations each model learns about the same test data point. If the representations are consistent, it means the model is reliable.When they compared their technique to state-of-the-art baseline methods, it was better at capturing the reliability of foundation models on a variety of downstream classification tasks.Someone could use this technique to decide if a model should be applied in a certain setting, without the need to test it on a real-world dataset. This could be especially useful when datasets may not be accessible due to privacy concerns, like in health care settings. In addition, the technique could be used to rank models based on reliability scores, enabling a user to select the best one for their task.“All models can be wrong, but models that know when they are wrong are more useful. The problem of quantifying uncertainty or reliability is more challenging for these foundation models because their abstract representations are difficult to compare. Our method allows one to quantify how reliable a representation model is for any given input data,” says senior author Navid Azizan, the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), and a member of the Laboratory for Information and Decision Systems (LIDS).He is joined on a paper about the work by lead author Young-Jin Park, a LIDS graduate student; Hao Wang, a research scientist at the MIT-IBM Watson AI Lab; and Shervin Ardeshir, a senior research scientist at Netflix. The paper will be presented at the Conference on Uncertainty in Artificial Intelligence.Measuring consensusTraditional machine-learning models are trained to perform a specific task. These models typically make a concrete prediction based on an input. For instance, the model might tell you whether a certain image contains a cat or a dog. In this case, assessing reliability could be a matter of looking at the final prediction to see if the model is right.But foundation models are different. The model is pretrained using general data, in a setting where its creators don’t know all downstream tasks it will be applied to. Users adapt it to their specific tasks after it has already been trained.Unlike traditional machine-learning models, foundation models don’t give concrete outputs like “cat” or “dog” labels. Instead, they generate an abstract representation based on an input data point.To assess the reliability of a foundation model, the researchers used an ensemble approach by training several models which share many properties but are slightly different from one another.“Our idea is like measuring the consensus. If all those foundation models are giving consistent representations for any data in our dataset, then we can say this model is reliable,” Park says.But they ran into a problem: How could they compare abstract representations?“These models just output a vector, comprised of some numbers, so we can’t compare them easily,” he adds.They solved this problem using an idea called neighborhood consistency.For their approach, the researchers prepare a set of reliable reference points to test on the ensemble of models. Then, for each model, they investigate the reference points located near that model’s representation of the test point.By looking at the consistency of neighboring points, they can estimate the reliability of the models.Aligning the representationsFoundation models map data points to what is known as a representation space. One way to think about this space is as a sphere. Each model maps similar data points to the same part of its sphere, so images of cats go in one place and images of dogs go in another.But each model would map animals differently in its own sphere, so while cats may be grouped near the South Pole of one sphere, another model could map cats somewhere in the Northern Hemisphere.The researchers use the neighboring points like anchors to align those spheres so they can make the representations comparable. If a data point’s neighbors are consistent across multiple representations, then one should be confident about the reliability of the model’s output for that point.When they tested this approach on a wide range of classification tasks, they found that it was much more consistent than baselines. Plus, it wasn’t tripped up by challenging test points that caused other methods to fail.Moreover, their approach can be used to assess reliability for any input data, so one could evaluate how well a model works for a particular type of individual, such as a patient with certain characteristics.“Even if the models all have average performance overall, from an individual point of view, you’d prefer the one that works best for that individual,” Wang says.However, one limitation comes from the fact that they must train an ensemble of foundation models, which is computationally expensive. In the future, they plan to find more efficient ways to build multiple models, perhaps by using small perturbations of a single model.“With the current trend of using foundational models for their embeddings to support various downstream tasks — from fine-tuning to retrieval augmented generation — the topic of quantifying uncertainty at the representation level is increasingly important, but challenging, as embeddings on their own have no grounding. What matters instead is how embeddings of different inputs are related to one another, an idea that this work neatly captures through the proposed neighborhood consistency score,” says Marco Pavone, an associate professor in the Department of Aeronautics and Astronautics at Stanford University, who was not involved with this work. “This is a promising step towards high quality uncertainty quantifications for embedding models, and I’m excited to see future extensions which can operate without requiring model-ensembling to really enable this approach to scale to foundation-size models.”This work is funded, in part, by the MIT-IBM Watson AI Lab, MathWorks, and Amazon. More

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      A technique for more effective multipurpose robots

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      Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.Existing robotic datasets vary widely in modality — some include color images while others are composed of tactile imprints, for instance. Data could also be collected in different domains, like simulation or human demos. And each dataset may capture a unique task and environment.It is difficult to efficiently incorporate data from so many sources in one machine-learning model, so many methods use just one type of data to train a robot. But robots trained this way, with a relatively small amount of task-specific data, are often unable to perform new tasks in unfamiliar environments.In an effort to train better multipurpose robots, MIT researchers developed a technique to combine multiple sources of data across domains, modalities, and tasks using a type of generative AI known as diffusion models.They train a separate diffusion model to learn a strategy, or policy, for completing one task using one specific dataset. Then they combine the policies learned by the diffusion models into a general policy that enables a robot to perform multiple tasks in various settings.In simulations and real-world experiments, this training approach enabled a robot to perform multiple tool-use tasks and adapt to new tasks it did not see during training. The method, known as Policy Composition (PoCo), led to a 20 percent improvement in task performance when compared to baseline techniques.“Addressing heterogeneity in robotic datasets is like a chicken-egg problem. If we want to use a lot of data to train general robot policies, then we first need deployable robots to get all this data. I think that leveraging all the heterogeneous data available, similar to what researchers have done with ChatGPT, is an important step for the robotics field,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on PoCo.     Wang’s coauthors include Jialiang Zhao, a mechanical engineering graduate student; Yilun Du, an EECS graduate student; Edward Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of CSAIL. The research will be presented at the Robotics: Science and Systems Conference.Combining disparate datasetsA robotic policy is a machine-learning model that takes inputs and uses them to perform an action. One way to think about a policy is as a strategy. In the case of a robotic arm, that strategy might be a trajectory, or a series of poses that move the arm so it picks up a hammer and uses it to pound a nail.Datasets used to learn robotic policies are typically small and focused on one particular task and environment, like packing items into boxes in a warehouse.“Every single robotic warehouse is generating terabytes of data, but it only belongs to that specific robot installation working on those packages. It is not ideal if you want to use all of these data to train a general machine,” Wang says.The MIT researchers developed a technique that can take a series of smaller datasets, like those gathered from many robotic warehouses, learn separate policies from each one, and combine the policies in a way that enables a robot to generalize to many tasks.They represent each policy using a type of generative AI model known as a diffusion model. Diffusion models, often used for image generation, learn to create new data samples that resemble samples in a training dataset by iteratively refining their output.But rather than teaching a diffusion model to generate images, the researchers teach it to generate a trajectory for a robot. They do this by adding noise to the trajectories in a training dataset. The diffusion model gradually removes the noise and refines its output into a trajectory.This technique, known as Diffusion Policy, was previously introduced by researchers at MIT, Columbia University, and the Toyota Research Institute. PoCo builds off this Diffusion Policy work. The team trains each diffusion model with a different type of dataset, such as one with human video demonstrations and another gleaned from teleoperation of a robotic arm.Then the researchers perform a weighted combination of the individual policies learned by all the diffusion models, iteratively refining the output so the combined policy satisfies the objectives of each individual policy.Greater than the sum of its parts“One of the benefits of this approach is that we can combine policies to get the best of both worlds. For instance, a policy trained on real-world data might be able to achieve more dexterity, while a policy trained on simulation might be able to achieve more generalization,” Wang says.

      With policy composition, researchers are able to combine datasets from multiple sources so they can teach a robot to effectively use a wide range of tools, like a hammer, screwdriver, or this spatula.Image: Courtesy of the researchers

      Because the policies are trained separately, one could mix and match diffusion policies to achieve better results for a certain task. A user could also add data in a new modality or domain by training an additional Diffusion Policy with that dataset, rather than starting the entire process from scratch.

      The policy composition technique the researchers developed can be used to effectively teach a robot to use tools even when objects are placed around it to try and distract it from its task, as seen here.Image: Courtesy of the researchers

      The researchers tested PoCo in simulation and on real robotic arms that performed a variety of tools tasks, such as using a hammer to pound a nail and flipping an object with a spatula. PoCo led to a 20 percent improvement in task performance compared to baseline methods.“The striking thing was that when we finished tuning and visualized it, we can clearly see that the composed trajectory looks much better than either one of them individually,” Wang says.In the future, the researchers want to apply this technique to long-horizon tasks where a robot would pick up one tool, use it, then switch to another tool. They also want to incorporate larger robotics datasets to improve performance.“We will need all three kinds of data to succeed for robotics: internet data, simulation data, and real robot data. How to combine them effectively will be the million-dollar question. PoCo is a solid step on the right track,” says Jim Fan, senior research scientist at NVIDIA and leader of the AI Agents Initiative, who was not involved with this work.This research is funded, in part, by Amazon, the Singapore Defense Science and Technology Agency, the U.S. National Science Foundation, and the Toyota Research Institute. More

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      Advancing technology for aquaculture

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      According to the National Oceanic and Atmospheric Administration, aquaculture in the United States represents a $1.5 billion industry annually. Like land-based farming, shellfish aquaculture requires healthy seed production in order to maintain a sustainable industry. Aquaculture hatchery production of shellfish larvae — seeds — requires close monitoring to track mortality rates and assess health from the earliest stages of life. 

      Careful observation is necessary to inform production scheduling, determine effects of naturally occurring harmful bacteria, and ensure sustainable seed production. This is an essential step for shellfish hatcheries but is currently a time-consuming manual process prone to human error. 

      With funding from MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), MIT Sea Grant is working with Associate Professor Otto Cordero of the MIT Department of Civil and Environmental Engineering, Professor Taskin Padir and Research Scientist Mark Zolotas at the Northeastern University Institute for Experiential Robotics, and others at the Aquaculture Research Corporation (ARC), and the Cape Cod Commercial Fishermen’s Alliance, to advance technology for the aquaculture industry. Located on Cape Cod, ARC is a leading shellfish hatchery, farm, and wholesaler that plays a vital role in providing high-quality shellfish seed to local and regional growers.

      Two MIT students have joined the effort this semester, working with Robert Vincent, MIT Sea Grant’s assistant director of advisory services, through the Undergraduate Research Opportunities Program (UROP). 

      First-year student Unyime Usua and sophomore Santiago Borrego are using microscopy images of shellfish seed from ARC to train machine learning algorithms that will help automate the identification and counting process. The resulting user-friendly image recognition tool aims to aid aquaculturists in differentiating and counting healthy, unhealthy, and dead shellfish larvae, improving accuracy and reducing time and effort.

      Vincent explains that AI is a powerful tool for environmental science that enables researchers, industry, and resource managers to address challenges that have long been pinch points for accurate data collection, analysis, predictions, and streamlining processes. “Funding support from programs like J-WAFS enable us to tackle these problems head-on,” he says. 

      ARC faces challenges with manually quantifying larvae classes, an important step in their seed production process. “When larvae are in their growing stages they are constantly being sized and counted,” explains Cheryl James, ARC larval/juvenile production manager. “This process is critical to encourage optimal growth and strengthen the population.” 

      Developing an automated identification and counting system will help to improve this step in the production process with time and cost benefits. “This is not an easy task,” says Vincent, “but with the guidance of Dr. Zolotas at the Northeastern University Institute for Experiential Robotics and the work of the UROP students, we have made solid progress.” 

      The UROP program benefits both researchers and students. Involving MIT UROP students in developing these types of systems provides insights into AI applications that they might not have considered, providing opportunities to explore, learn, and apply themselves while contributing to solving real challenges.

      Borrego saw this project as an opportunity to apply what he’d learned in class 6.390 (Introduction to Machine Learning) to a real-world issue. “I was starting to form an idea of how computers can see images and extract information from them,” he says. “I wanted to keep exploring that.”

      Usua decided to pursue the project because of the direct industry impacts it could have. “I’m pretty interested in seeing how we can utilize machine learning to make people’s lives easier. We are using AI to help biologists make this counting and identification process easier.” While Usua wasn’t familiar with aquaculture before starting this project, she explains, “Just hearing about the hatcheries that Dr. Vincent was telling us about, it was unfortunate that not a lot of people know what’s going on and the problems that they’re facing.”

      On Cape Cod alone, aquaculture is an $18 million per year industry. But the Massachusetts Division of Marine Fisheries estimates that hatcheries are only able to meet 70–80 percent of seed demand annually, which impacts local growers and economies. Through this project, the partners aim to develop technology that will increase seed production, advance industry capabilities, and help understand and improve the hatchery microbiome.

      Borrego explains the initial challenge of having limited data to work with. “Starting out, we had to go through and label all of the data, but going through that process helped me learn a lot.” In true MIT fashion, he shares his takeaway from the project: “Try to get the best out of what you’re given with the data you have to work with. You’re going to have to adapt and change your strategies depending on what you have.”

      Usua describes her experience going through the research process, communicating in a team, and deciding what approaches to take. “Research is a difficult and long process, but there is a lot to gain from it because it teaches you to look for things on your own and find your own solutions to problems.”

      In addition to increasing seed production and reducing the human labor required in the hatchery process, the collaborators expect this project to contribute to cost savings and technology integration to support one of the most underserved industries in the United States. 

      Borrego and Usua both plan to continue their work for a second semester with MIT Sea Grant. Borrego is interested in learning more about how technology can be used to protect the environment and wildlife. Usua says she hopes to explore more projects related to aquaculture. “It seems like there’s an infinite amount of ways to tackle these issues.” More

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      MIT researchers remotely map crops, field by field

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      Crop maps help scientists and policymakers track global food supplies and estimate how they might shift with climate change and growing populations. But getting accurate maps of the types of crops that are grown from farm to farm often requires on-the-ground surveys that only a handful of countries have the resources to maintain.

      Now, MIT engineers have developed a method to quickly and accurately label and map crop types without requiring in-person assessments of every single farm. The team’s method uses a combination of Google Street View images, machine learning, and satellite data to automatically determine the crops grown throughout a region, from one fraction of an acre to the next. 

      The researchers used the technique to automatically generate the first nationwide crop map of Thailand — a smallholder country where small, independent farms make up the predominant form of agriculture. The team created a border-to-border map of Thailand’s four major crops — rice, cassava, sugarcane, and maize — and determined which of the four types was grown, at every 10 meters, and without gaps, across the entire country. The resulting map achieved an accuracy of 93 percent, which the researchers say is comparable to on-the-ground mapping efforts in high-income, big-farm countries.

      The team is applying their mapping technique to other countries such as India, where small farms sustain most of the population but the type of crops grown from farm to farm has historically been poorly recorded.

      “It’s a longstanding gap in knowledge about what is grown around the world,” says Sherrie Wang, the d’Arbeloff Career Development Assistant Professor in MIT’s Department of Mechanical Engineering, and the Institute for Data, Systems, and Society (IDSS). “The final goal is to understand agricultural outcomes like yield, and how to farm more sustainably. One of the key preliminary steps is to map what is even being grown — the more granularly you can map, the more questions you can answer.”

      Wang, along with MIT graduate student Jordi Laguarta Soler and Thomas Friedel of the agtech company PEAT GmbH, will present a paper detailing their mapping method later this month at the AAAI Conference on Artificial Intelligence.

      Ground truth

      Smallholder farms are often run by a single family or farmer, who subsist on the crops and livestock that they raise. It’s estimated that smallholder farms support two-thirds of the world’s rural population and produce 80 percent of the world’s food. Keeping tabs on what is grown and where is essential to tracking and forecasting food supplies around the world. But the majority of these small farms are in low to middle-income countries, where few resources are devoted to keeping track of individual farms’ crop types and yields.

      Crop mapping efforts are mainly carried out in high-income regions such as the United States and Europe, where government agricultural agencies oversee crop surveys and send assessors to farms to label crops from field to field. These “ground truth” labels are then fed into machine-learning models that make connections between the ground labels of actual crops and satellite signals of the same fields. They then label and map wider swaths of farmland that assessors don’t cover but that satellites automatically do.

      “What’s lacking in low- and middle-income countries is this ground label that we can associate with satellite signals,” Laguarta Soler says. “Getting these ground truths to train a model in the first place has been limited in most of the world.”

      The team realized that, while many developing countries do not have the resources to maintain crop surveys, they could potentially use another source of ground data: roadside imagery, captured by services such as Google Street View and Mapillary, which send cars throughout a region to take continuous 360-degree images with dashcams and rooftop cameras.

      In recent years, such services have been able to access low- and middle-income countries. While the goal of these services is not specifically to capture images of crops, the MIT team saw that they could search the roadside images to identify crops.

      Cropped image

      In their new study, the researchers worked with Google Street View (GSV) images taken throughout Thailand — a country that the service has recently imaged fairly thoroughly, and which consists predominantly of smallholder farms.

      Starting with over 200,000 GSV images randomly sampled across Thailand, the team filtered out images that depicted buildings, trees, and general vegetation. About 81,000 images were crop-related. They set aside 2,000 of these, which they sent to an agronomist, who determined and labeled each crop type by eye. They then trained a convolutional neural network to automatically generate crop labels for the other 79,000 images, using various training methods, including iNaturalist — a web-based crowdsourced  biodiversity database, and GPT-4V, a “multimodal large language model” that enables a user to input an image and ask the model to identify what the image is depicting. For each of the 81,000 images, the model generated a label of one of four crops that the image was likely depicting — rice, maize, sugarcane, or cassava.

      The researchers then paired each labeled image with the corresponding satellite data taken of the same location throughout a single growing season. These satellite data include measurements across multiple wavelengths, such as a location’s greenness and its reflectivity (which can be a sign of water). 

      “Each type of crop has a certain signature across these different bands, which changes throughout a growing season,” Laguarta Soler notes.

      The team trained a second model to make associations between a location’s satellite data and its corresponding crop label. They then used this model to process satellite data taken of the rest of the country, where crop labels were not generated or available. From the associations that the model learned, it then assigned crop labels across Thailand, generating a country-wide map of crop types, at a resolution of 10 square meters.

      This first-of-its-kind crop map included locations corresponding to the 2,000 GSV images that the researchers originally set aside, that were labeled by arborists. These human-labeled images were used to validate the map’s labels, and when the team looked to see whether the map’s labels matched the expert, “gold standard” labels, it did so 93 percent of the time.

      “In the U.S., we’re also looking at over 90 percent accuracy, whereas with previous work in India, we’ve only seen 75 percent because ground labels are limited,” Wang says. “Now we can create these labels in a cheap and automated way.”

      The researchers are moving to map crops across India, where roadside images via Google Street View and other services have recently become available.

      “There are over 150 million smallholder farmers in India,” Wang says. “India is covered in agriculture, almost wall-to-wall farms, but very small farms, and historically it’s been very difficult to create maps of India because there are very sparse ground labels.”

      The team is working to generate crop maps in India, which could be used to inform policies having to do with assessing and bolstering yields, as global temperatures and populations rise.

      “What would be interesting would be to create these maps over time,” Wang says. “Then you could start to see trends, and we can try to relate those things to anything like changes in climate and policies.” More

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      Six MIT students selected as spring 2024 MIT-Pillar AI Collective Fellows

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      The MIT-Pillar AI Collective has announced six fellows for the spring 2024 semester. With support from the program, the graduate students, who are in their final year of a master’s or PhD program, will conduct research in the areas of AI, machine learning, and data science with the aim of commercializing their innovations.

      Launched by MIT’s School of Engineering and Pillar VC in 2022, the MIT-Pillar AI Collective supports faculty, postdocs, and students conducting research on AI, machine learning, and data science. Supported by a gift from Pillar VC and administered by the MIT Deshpande Center for Technological Innovation, the mission of the program is to advance research toward commercialization.

      The spring 2024 MIT-Pillar AI Collective Fellows are:

      Yasmeen AlFaraj

      Yasmeen AlFaraj is a PhD candidate in chemistry whose interest is in the application of data science and machine learning to soft materials design to enable next-generation, sustainable plastics, rubber, and composite materials. More specifically, she is applying machine learning to the design of novel molecular additives to enable the low-cost manufacturing of chemically deconstructable thermosets and composites. AlFaraj’s work has led to the discovery of scalable, translatable new materials that could address thermoset plastic waste. As a Pillar Fellow, she will pursue bringing this technology to market, initially focusing on wind turbine blade manufacturing and conformal coatings. Through the Deshpande Center for Technological Innovation, AlFaraj serves as a lead for a team developing a spinout focused on recyclable versions of existing high-performance thermosets by incorporating small quantities of a degradable co-monomer. In addition, she participated in the National Science Foundation Innovation Corps program and recently graduated from the Clean Tech Open, where she focused on enhancing her business plan, analyzing potential markets, ensuring a complete IP portfolio, and connecting with potential funders. AlFaraj earned a BS in chemistry from University of California at Berkeley.

      Ruben Castro Ornelas

      Ruben Castro Ornelas is a PhD student in mechanical engineering who is passionate about the future of multipurpose robots and designing the hardware to use them with AI control solutions. Combining his expertise in programming, embedded systems, machine design, reinforcement learning, and AI, he designed a dexterous robotic hand capable of carrying out useful everyday tasks without sacrificing size, durability, complexity, or simulatability. Ornelas’s innovative design holds significant commercial potential in domestic, industrial, and health-care applications because it could be adapted to hold everything from kitchenware to delicate objects. As a Pillar Fellow, he will focus on identifying potential commercial markets, determining the optimal approach for business-to-business sales, and identifying critical advisors. Ornelas served as co-director of StartLabs, an undergraduate entrepreneurship club at MIT, where he earned an BS in mechanical engineering.

      Keeley Erhardt

      Keeley Erhardt is a PhD candidate in media arts and sciences whose research interests lie in the transformative potential of AI in network analysis, particularly for entity correlation and hidden link detection within and across domains. She has designed machine learning algorithms to identify and track temporal correlations and hidden signals in large-scale networks, uncovering online influence campaigns originating from multiple countries. She has similarly demonstrated the use of graph neural networks to identify coordinated cryptocurrency accounts by analyzing financial time series data and transaction dynamics. As a Pillar Fellow, Erhardt will pursue the potential commercial applications of her work, such as detecting fraud, propaganda, money laundering, and other covert activity in the finance, energy, and national security sectors. She has had internships at Google, Facebook, and Apple and held software engineering roles at multiple tech unicorns. Erhardt earned an MEng in electrical engineering and computer science and a BS in computer science, both from MIT.

      Vineet Jagadeesan Nair

      Vineet Jagadeesan Nair is a PhD candidate in mechanical engineering whose research focuses on modeling power grids and designing electricity markets to integrate renewables, batteries, and electric vehicles. He is broadly interested in developing computational tools to tackle climate change. As a Pillar Fellow, Nair will explore the application of machine learning and data science to power systems. Specifically, he will experiment with approaches to improve the accuracy of forecasting electricity demand and supply with high spatial-temporal resolution. In collaboration with Project Tapestry @ Google X, he is also working on fusing physics-informed machine learning with conventional numerical methods to increase the speed and accuracy of high-fidelity simulations. Nair’s work could help realize future grids with high penetrations of renewables and other clean, distributed energy resources. Outside academics, Nair is active in entrepreneurship, most recently helping to organize the 2023 MIT Global Startup Workshop in Greece. He earned an MS in computational science and engineering from MIT, an MPhil in energy technologies from Cambridge University as a Gates Scholar, and a BS in mechanical engineering and a BA in economics from University of California at Berkeley.

      Mahdi Ramadan

      Mahdi Ramadan is a PhD candidate in brain and cognitive sciences whose research interests lie at the intersection of cognitive science, computational modeling, and neural technologies. His work uses novel unsupervised methods for learning and generating interpretable representations of neural dynamics, capitalizing on recent advances in AI, specifically contrastive and geometric deep learning techniques capable of uncovering the latent dynamics underlying neural processes with high fidelity. As a Pillar Fellow, he will leverage these methods to gain a better understanding of dynamical models of muscle signals for generative motor control. By supplementing current spinal prosthetics with generative AI motor models that can streamline, speed up, and correct limb muscle activations in real time, as well as potentially using multimodal vision-language models to infer the patients’ high-level intentions, Ramadan aspires to build truly scalable, accessible, and capable commercial neuroprosthetics. Ramadan’s entrepreneurial experience includes being the co-founder of UltraNeuro, a neurotechnology startup, and co-founder of Presizely, a computer vision startup. He earned a BS in neurobiology from University of Washington.

      Rui (Raymond) Zhou

      Rui (Raymond) Zhou is a PhD candidate in mechanical engineering whose research focuses on multimodal AI for engineering design. As a Pillar Fellow, he will advance models that could enable designers to translate information in any modality or combination of modalities into comprehensive 2D and 3D designs, including parametric data, component visuals, assembly graphs, and sketches. These models could also optimize existing human designs to accomplish goals such as improving ergonomics or reducing drag coefficient. Ultimately, Zhou aims to translate his work into a software-as-a-service platform that redefines product design across various sectors, from automotive to consumer electronics. His efforts have the potential to not only accelerate the design process but also reduce costs, opening the door to unprecedented levels of customization, idea generation, and rapid prototyping. Beyond his academic pursuits, Zhou founded UrsaTech, a startup that integrates AI into education and engineering design. He earned a BS in electrical engineering and computer sciences from University of California at Berkeley. More

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      New model predicts how shoe properties affect a runner’s performance

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      A good shoe can make a huge difference for runners, from career marathoners to couch-to-5K first-timers. But every runner is unique, and a shoe that works for one might trip up another. Outside of trying on a rack of different designs, there’s no quick and easy way to know which shoe best suits a person’s particular running style.

      MIT engineers are hoping to change that with a new model that predicts how certain shoe properties will affect a runner’s performance.

      The simple model incorporates a person’s height, weight, and other general dimensions, along with shoe properties such as stiffness and springiness along the midsole. With this input, the model then simulates a person’s running gait, or how they would run, in a particular shoe.

      Play video

      Using the model, the researchers can simulate how a runner’s gait changes with different shoe types. They can then pick out the shoe that produces the best performance, which they define as the degree to which a runner’s expended energy is minimized.

      While the model can accurately simulate changes in a runner’s gait when comparing two very different shoe types, it is less discerning when comparing relatively similar designs, including most commercially available running shoes. For this reason, the researchers envision the current model would be best used as a tool for shoe designers looking to push the boundaries of sneaker design.

      “Shoe designers are starting to 3D print shoes, meaning they can now make them with a much wider range of properties than with just a regular slab of foam,” says Sarah Fay, a postdoc in MIT’s Sports Lab and the Institute for Data, Systems, and Society (IDSS). “Our model could help them design really novel shoes that are also high-performing.”

      The team is planning to improve the model, in hopes that consumers can one day use a similar version to pick shoes that fit their personal running style.

      “We’ve allowed for enough flexibility in the model that it can be used to design custom shoes and understand different individual behaviors,” Fay says. “Way down the road, we imagine that if you send us a video of yourself running, we could 3D print the shoe that’s right for you. That would be the moonshot.”

      The new model is reported in a study appearing this month in the Journal of Biomechanical Engineering. The study is authored by Fay and Anette “Peko” Hosoi, professor of mechanical engineering at MIT.

      Running, revamped

      The team’s new model grew out of talks with collaborators in the sneaker industry, where designers have started to 3D print shoes at commercial scale. These designs incorporate 3D-printed midsoles that resemble intricate scaffolds, the geometry of which can be tailored to give a certain bounce or stiffness in specific locations across the sole.

      “With 3D printing, designers can tune everything about the material response locally,” Hosoi says. “And they came to us and essentially said, ‘We can do all these things. What should we do?’”

      “Part of the design problem is to predict what a runner will do when you put an entirely new shoe on them,” Fay adds. “You have to couple the dynamics of the runner with the properties of the shoe.”

      Fay and Hosoi looked first to represent a runner’s dynamics using a simple model. They drew inspiration from Thomas McMahon, a leader in the study of biomechanics at Harvard University, who in the 1970s used a very simple “spring and damper” model to model a runner’s essential gait mechanics. Using this gait model, he predicted how fast a person could run on various track types, from traditional concrete surfaces to more rubbery material. The model showed that runners should run faster on softer, bouncier tracks that supported a runner’s natural gait.

      Though this may be unsurprising today, the insight was a revelation at the time, prompting Harvard to revamp its indoor track — a move that quickly accumulated track records, as runners found they could run much faster on the softier, springier surface.

      “McMahon’s work showed that, even if we don’t model every single limb and muscle and component of the human body, we’re still able to create meaningful insights in terms of how we design for athletic performance,” Fay says.

      Gait cost

      Following McMahon’s lead, Fay and Hosoi developed a similar, simplified model of a runner’s dynamics. The model represents a runner as a center of mass, with a hip that can rotate and a leg that can stretch. The leg is connected to a box-like shoe, with springiness and shock absorption that can be tuned, both vertically and horizontally.

      They reasoned that they should be able to input into the model a person’s basic dimensions, such as their height, weight, and leg length, along with a shoe’s material properties, such as the stiffness of the front and back midsole, and use the model to simulate what a person’s gait is likely to be when running in that shoe.

      But they also realized that a person’s gait can depend on a less definable property, which they call the “biological cost function” — a quality that a runner might not consciously be aware of but nevertheless may try to minimize whenever they run. The team reasoned that if they can identify a biological cost function that is general to most runners, then they might predict not only a person’s gait for a given shoe but also which shoe produces the gait corresponding to the best running performance.

      With this in mind, the team looked to a previous treadmill study, which recorded detailed measurements of runners, such as the force of their impacts, the angle and motion of their joints, the spring in their steps, and the work of their muscles as they ran, each in the same type of running shoe.

      Fay and Hosoi hypothesized that each runner’s actual gait arose not only from their personal dimensions and shoe properties, but also a subconscious goal to minimize one or more biological measures, yet unknown. To reveal these measures, the team used their model to simulate each runner’s gait multiple times. Each time, they programmed the model to assume the runner minimized a different biological cost, such as the degree to which they swing their leg or the impact that they make with the treadmill. They then compared the modeled gait with the runner’s actual gait to see which modeled gait — and assumed cost — matched the actual gait.

      In the end, the team found that most runners tend to minimize two costs: the impact their feet make with the treadmill and the amount of energy their legs expend.

      “If we tell our model, ‘Optimize your gait on these two things,’ it gives us really realistic-looking gaits that best match the data we have,” Fay explains. “This gives us confidence that the model can predict how people will actually run, even if we change their shoe.”

      As a final step, the researchers simulated a wide range of shoe styles and used the model to predict a runner’s gait and how efficient each gait would be for a given type of shoe.

      “In some ways, this gives you a quantitative way to design a shoe for a 10K versus a marathon shoe,” Hosoi says. “Designers have an intuitive sense for that. But now we have a mathematical understanding that we hope designers can use as a tool to kickstart new ideas.”

      This research is supported, in part, by adidas. More

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      in Data Management & Statistics

      Self-powered sensor automatically harvests magnetic energy

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      MIT researchers have developed a battery-free, self-powered sensor that can harvest energy from its environment.

      Because it requires no battery that must be recharged or replaced, and because it requires no special wiring, such a sensor could be embedded in a hard-to-reach place, like inside the inner workings of a ship’s engine. There, it could automatically gather data on the machine’s power consumption and operations for long periods of time.

      The researchers built a temperature-sensing device that harvests energy from the magnetic field generated in the open air around a wire. One could simply clip the sensor around a wire that carries electricity — perhaps the wire that powers a motor — and it will automatically harvest and store energy which it uses to monitor the motor’s temperature.

      “This is ambient power — energy that I don’t have to make a specific, soldered connection to get. And that makes this sensor very easy to install,” says Steve Leeb, the Emanuel E. Landsman Professor of Electrical Engineering and Computer Science (EECS) and professor of mechanical engineering, a member of the Research Laboratory of Electronics, and senior author of a paper on the energy-harvesting sensor.

      In the paper, which appeared as the featured article in the January issue of the IEEE Sensors Journal, the researchers offer a design guide for an energy-harvesting sensor that lets an engineer balance the available energy in the environment with their sensing needs.

      The paper lays out a roadmap for the key components of a device that can sense and control the flow of energy continually during operation.

      The versatile design framework is not limited to sensors that harvest magnetic field energy, and can be applied to those that use other power sources, like vibrations or sunlight. It could be used to build networks of sensors for factories, warehouses, and commercial spaces that cost less to install and maintain.

      “We have provided an example of a battery-less sensor that does something useful, and shown that it is a practically realizable solution. Now others will hopefully use our framework to get the ball rolling to design their own sensors,” says lead author Daniel Monagle, an EECS graduate student.

      Monagle and Leeb are joined on the paper by EECS graduate student Eric Ponce.

      John Donnal, an associate professor of weapons and controls engineering at the U.S. Naval Academy who was not involved with this work, studies techniques to monitor ship systems. Getting access to power on a ship can be difficult, he says, since there are very few outlets and strict restrictions as to what equipment can be plugged in.

      “Persistently measuring the vibration of a pump, for example, could give the crew real-time information on the health of the bearings and mounts, but powering a retrofit sensor often requires so much additional infrastructure that the investment is not worthwhile,” Donnal adds. “Energy-harvesting systems like this could make it possible to retrofit a wide variety of diagnostic sensors on ships and significantly reduce the overall cost of maintenance.”

      A how-to guide

      The researchers had to meet three key challenges to develop an effective, battery-free, energy-harvesting sensor.

      First, the system must be able to cold start, meaning it can fire up its electronics with no initial voltage. They accomplished this with a network of integrated circuits and transistors that allow the system to store energy until it reaches a certain threshold. The system will only turn on once it has stored enough power to fully operate.

      Second, the system must store and convert the energy it harvests efficiently, and without a battery. While the researchers could have included a battery, that would add extra complexities to the system and could pose a fire risk.

      “You might not even have the luxury of sending out a technician to replace a battery. Instead, our system is maintenance-free. It harvests energy and operates itself,” Monagle adds.

      To avoid using a battery, they incorporate internal energy storage that can include a series of capacitors. Simpler than a battery, a capacitor stores energy in the electrical field between conductive plates. Capacitors can be made from a variety of materials, and their capabilities can be tuned to a range of operating conditions, safety requirements, and available space.

      The team carefully designed the capacitors so they are big enough to store the energy the device needs to turn on and start harvesting power, but small enough that the charge-up phase doesn’t take too long.

      In addition, since a sensor might go weeks or even months before turning on to take a measurement, they ensured the capacitors can hold enough energy even if some leaks out over time.

      Finally, they developed a series of control algorithms that dynamically measure and budget the energy collected, stored, and used by the device. A microcontroller, the “brain” of the energy management interface, constantly checks how much energy is stored and infers whether to turn the sensor on or off, take a measurement, or kick the harvester into a higher gear so it can gather more energy for more complex sensing needs.

      “Just like when you change gears on a bike, the energy management interface looks at how the harvester is doing, essentially seeing whether it is pedaling too hard or too soft, and then it varies the electronic load so it can maximize the amount of power it is harvesting and match the harvest to the needs of the sensor,” Monagle explains.

      Self-powered sensor

      Using this design framework, they built an energy management circuit for an off-the-shelf temperature sensor. The device harvests magnetic field energy and uses it to continually sample temperature data, which it sends to a smartphone interface using Bluetooth.

      The researchers used super-low-power circuits to design the device, but quickly found that these circuits have tight restrictions on how much voltage they can withstand before breaking down. Harvesting too much power could cause the device to explode.

      To avoid that, their energy harvester operating system in the microcontroller automatically adjusts or reduces the harvest if the amount of stored energy becomes excessive.

      They also found that communication — transmitting data gathered by the temperature sensor — was by far the most power-hungry operation.

      “Ensuring the sensor has enough stored energy to transmit data is a constant challenge that involves careful design,” Monagle says.

      In the future, the researchers plan to explore less energy-intensive means of transmitting data, such as using optics or acoustics. They also want to more rigorously model and predict how much energy might be coming into a system, or how much energy a sensor might need to take measurements, so a device could effectively gather even more data.

      “If you only make the measurements you think you need, you may miss something really valuable. With more information, you might be able to learn something you didn’t expect about a device’s operations. Our framework lets you balance those considerations,” Leeb says.  

      “This paper is well-documented regarding what a practical self-powered sensor node should internally entail for realistic scenarios. The overall design guidelines, particularly on the cold-start issue, are very helpful,” says Jinyeong Moon, an assistant professor of electrical and computer engineering at Florida State University College of Engineering who was not involved with this work. “Engineers planning to design a self-powering module for a wireless sensor node will greatly benefit from these guidelines, easily ticking off traditionally cumbersome cold-start-related checklists.”

      The work is supported, in part, by the Office of Naval Research and The Grainger Foundation. More

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      in Data Management & Statistics

      2023-24 Takeda Fellows: Advancing research at the intersection of AI and health

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      The School of Engineering has selected 13 new Takeda Fellows for the 2023-24 academic year. With support from Takeda, the graduate students will conduct pathbreaking research ranging from remote health monitoring for virtual clinical trials to ingestible devices for at-home, long-term diagnostics.

      Now in its fourth year, the MIT-Takeda Program, a collaboration between MIT’s School of Engineering and Takeda, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry.

      The 2023-24 Takeda Fellows are:

      Adam Gierlach

      Adam Gierlach is a PhD candidate in the Department of Electrical Engineering and Computer Science. Gierlach’s work combines innovative biotechnology with machine learning to create ingestible devices for advanced diagnostics and delivery of therapeutics. In his previous work, Gierlach developed a non-invasive, ingestible device for long-term gastric recordings in free-moving patients. With the support of a Takeda Fellowship, he will build on this pathbreaking work by developing smart, energy-efficient, ingestible devices powered by application-specific integrated circuits for at-home, long-term diagnostics. These revolutionary devices — capable of identifying, characterizing, and even correcting gastrointestinal diseases — represent the leading edge of biotechnology. Gierlach’s innovative contributions will help to advance fundamental research on the enteric nervous system and help develop a better understanding of gut-brain axis dysfunctions in Parkinson’s disease, autism spectrum disorder, and other prevalent disorders and conditions.

      Vivek Gopalakrishnan

      Vivek Gopalakrishnan is a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology. Gopalakrishnan’s goal is to develop biomedical machine-learning methods to improve the study and treatment of human disease. Specifically, he employs computational modeling to advance new approaches for minimally invasive, image-guided neurosurgery, offering a safe alternative to open brain and spinal procedures. With the support of a Takeda Fellowship, Gopalakrishnan will develop real-time computer vision algorithms that deliver high-quality, 3D intraoperative image guidance by extracting and fusing information from multimodal neuroimaging data. These algorithms could allow surgeons to reconstruct 3D neurovasculature from X-ray angiography, thereby enhancing the precision of device deployment and enabling more accurate localization of healthy versus pathologic anatomy.

      Hao He

      Hao He is a PhD candidate in the Department of Electrical Engineering and Computer Science. His research interests lie at the intersection of generative AI, machine learning, and their applications in medicine and human health, with a particular emphasis on passive, continuous, remote health monitoring to support virtual clinical trials and health-care management. More specifically, He aims to develop trustworthy AI models that promote equitable access and deliver fair performance independent of race, gender, and age. In his past work, He has developed monitoring systems applied in clinical studies of Parkinson’s disease, Alzheimer’s disease, and epilepsy. Supported by a Takeda Fellowship, He will develop a novel technology for the passive monitoring of sleep stages (using radio signaling) that seeks to address existing gaps in performance across different demographic groups. His project will tackle the problem of imbalance in available datasets and account for intrinsic differences across subpopulations, using generative AI and multi-modality/multi-domain learning, with the goal of learning robust features that are invariant to different subpopulations. He’s work holds great promise for delivering advanced, equitable health-care services to all people and could significantly impact health care and AI.

      Chengyi Long

      Chengyi Long is a PhD candidate in the Department of Civil and Environmental Engineering. Long’s interdisciplinary research integrates the methodology of physics, mathematics, and computer science to investigate questions in ecology. Specifically, Long is developing a series of potentially groundbreaking techniques to explain and predict the temporal dynamics of ecological systems, including human microbiota, which are essential subjects in health and medical research. His current work, supported by a Takeda Fellowship, is focused on developing a conceptual, mathematical, and practical framework to understand the interplay between external perturbations and internal community dynamics in microbial systems, which may serve as a key step toward finding bio solutions to health management. A broader perspective of his research is to develop AI-assisted platforms to anticipate the changing behavior of microbial systems, which may help to differentiate between healthy and unhealthy hosts and design probiotics for the prevention and mitigation of pathogen infections. By creating novel methods to address these issues, Long’s research has the potential to offer powerful contributions to medicine and global health.

      Omar Mohd

      Omar Mohd is a PhD candidate in the Department of Electrical Engineering and Computer Science. Mohd’s research is focused on developing new technologies for the spatial profiling of microRNAs, with potentially important applications in cancer research. Through innovative combinations of micro-technologies and AI-enabled image analysis to measure the spatial variations of microRNAs within tissue samples, Mohd hopes to gain new insights into drug resistance in cancer. This work, supported by a Takeda Fellowship, falls within the emerging field of spatial transcriptomics, which seeks to understand cancer and other diseases by examining the relative locations of cells and their contents within tissues. The ultimate goal of Mohd’s current project is to find multidimensional patterns in tissues that may have prognostic value for cancer patients. One valuable component of his work is an open-source AI program developed with collaborators at Beth Israel Deaconess Medical Center and Harvard Medical School to auto-detect cancer epithelial cells from other cell types in a tissue sample and to correlate their abundance with the spatial variations of microRNAs. Through his research, Mohd is making innovative contributions at the interface of microsystem technology, AI-based image analysis, and cancer treatment, which could significantly impact medicine and human health.

      Sanghyun Park

      Sanghyun Park is a PhD candidate in the Department of Mechanical Engineering. Park specializes in the integration of AI and biomedical engineering to address complex challenges in human health. Drawing on his expertise in polymer physics, drug delivery, and rheology, his research focuses on the pioneering field of in-situ forming implants (ISFIs) for drug delivery. Supported by a Takeda Fellowship, Park is currently developing an injectable formulation designed for long-term drug delivery. The primary goal of his research is to unravel the compaction mechanism of drug particles in ISFI formulations through comprehensive modeling and in-vitro characterization studies utilizing advanced AI tools. He aims to gain a thorough understanding of this unique compaction mechanism and apply it to drug microcrystals to achieve properties optimal for long-term drug delivery. Beyond these fundamental studies, Park’s research also focuses on translating this knowledge into practical applications in a clinical setting through animal studies specifically aimed at extending drug release duration and improving mechanical properties. The innovative use of AI in developing advanced drug delivery systems, coupled with Park’s valuable insights into the compaction mechanism, could contribute to improving long-term drug delivery. This work has the potential to pave the way for effective management of chronic diseases, benefiting patients, clinicians, and the pharmaceutical industry.

      Huaiyao Peng

      Huaiyao Peng is a PhD candidate in the Department of Biological Engineering. Peng’s research interests are focused on engineered tissue, microfabrication platforms, cancer metastasis, and the tumor microenvironment. Specifically, she is advancing novel AI techniques for the development of pre-cancer organoid models of high-grade serous ovarian cancer (HGSOC), an especially lethal and difficult-to-treat cancer, with the goal of gaining new insights into progression and effective treatments. Peng’s project, supported by a Takeda Fellowship, will be one of the first to use cells from serous tubal intraepithelial carcinoma lesions found in the fallopian tubes of many HGSOC patients. By examining the cellular and molecular changes that occur in response to treatment with small molecule inhibitors, she hopes to identify potential biomarkers and promising therapeutic targets for HGSOC, including personalized treatment options for HGSOC patients, ultimately improving their clinical outcomes. Peng’s work has the potential to bring about important advances in cancer treatment and spur innovative new applications of AI in health care. 

      Priyanka Raghavan

      Priyanka Raghavan is a PhD candidate in the Department of Chemical Engineering. Raghavan’s research interests lie at the frontier of predictive chemistry, integrating computational and experimental approaches to build powerful new predictive tools for societally important applications, including drug discovery. Specifically, Raghavan is developing novel models to predict small-molecule substrate reactivity and compatibility in regimes where little data is available (the most realistic regimes). A Takeda Fellowship will enable Raghavan to push the boundaries of her research, making innovative use of low-data and multi-task machine learning approaches, synthetic chemistry, and robotic laboratory automation, with the goal of creating an autonomous, closed-loop system for the discovery of high-yielding organic small molecules in the context of underexplored reactions. Raghavan’s work aims to identify new, versatile reactions to broaden a chemist’s synthetic toolbox with novel scaffolds and substrates that could form the basis of essential drugs. Her work has the potential for far-reaching impacts in early-stage, small-molecule discovery and could help make the lengthy drug-discovery process significantly faster and cheaper.

      Zhiye Song

      Zhiye “Zoey” Song is a PhD candidate in the Department of Electrical Engineering and Computer Science. Song’s research integrates cutting-edge approaches in machine learning (ML) and hardware optimization to create next-generation, wearable medical devices. Specifically, Song is developing novel approaches for the energy-efficient implementation of ML computation in low-power medical devices, including a wearable ultrasound “patch” that captures and processes images for real-time decision-making capabilities. Her recent work, conducted in collaboration with clinicians, has centered on bladder volume monitoring; other potential applications include blood pressure monitoring, muscle diagnosis, and neuromodulation. With the support of a Takeda Fellowship, Song will build on that promising work and pursue key improvements to existing wearable device technologies, including developing low-compute and low-memory ML algorithms and low-power chips to enable ML on smart wearable devices. The technologies emerging from Song’s research could offer exciting new capabilities in health care, enabling powerful and cost-effective point-of-care diagnostics and expanding individual access to autonomous and continuous medical monitoring.

      Peiqi Wang

      Peiqi Wang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Wang’s research aims to develop machine learning methods for learning and interpretation from medical images and associated clinical data to support clinical decision-making. He is developing a multimodal representation learning approach that aligns knowledge captured in large amounts of medical image and text data to transfer this knowledge to new tasks and applications. Supported by a Takeda Fellowship, Wang will advance this promising line of work to build robust tools that interpret images, learn from sparse human feedback, and reason like doctors, with potentially major benefits to important stakeholders in health care.

      Oscar Wu

      Haoyang “Oscar” Wu is a PhD candidate in the Department of Chemical Engineering. Wu’s research integrates quantum chemistry and deep learning methods to accelerate the process of small-molecule screening in the development of new drugs. By identifying and automating reliable methods for finding transition state geometries and calculating barrier heights for new reactions, Wu’s work could make it possible to conduct the high-throughput ab initio calculations of reaction rates needed to screen the reactivity of large numbers of active pharmaceutical ingredients (APIs). A Takeda Fellowship will support his current project to: (1) develop open-source software for high-throughput quantum chemistry calculations, focusing on the reactivity of drug-like molecules, and (2) develop deep learning models that can quantitatively predict the oxidative stability of APIs. The tools and insights resulting from Wu’s research could help to transform and accelerate the drug-discovery process, offering significant benefits to the pharmaceutical and medical fields and to patients.

      Soojung Yang

      Soojung Yang is a PhD candidate in the Department of Materials Science and Engineering. Yang’s research applies cutting-edge methods in geometric deep learning and generative modeling, along with atomistic simulations, to better understand and model protein dynamics. Specifically, Yang is developing novel tools in generative AI to explore protein conformational landscapes that offer greater speed and detail than physics-based simulations at a substantially lower cost. With the support of a Takeda Fellowship, she will build upon her successful work on the reverse transformation of coarse-grained proteins to the all-atom resolution, aiming to build machine-learning models that bridge multiple size scales of protein conformation diversity (all-atom, residue-level, and domain-level). Yang’s research holds the potential to provide a powerful and widely applicable new tool for researchers who seek to understand the complex protein functions at work in human diseases and to design drugs to treat and cure those diseases.

      Yuzhe Yang

      Yuzhe Yang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Yang’s research interests lie at the intersection of machine learning and health care. In his past and current work, Yang has developed and applied innovative machine-learning models that address key challenges in disease diagnosis and tracking. His many notable achievements include the creation of one of the first machine learning-based solutions using nocturnal breathing signals to detect Parkinson’s disease (PD), estimate disease severity, and track PD progression. With the support of a Takeda Fellowship, Yang will expand this promising work to develop an AI-based diagnosis model for Alzheimer’s disease (AD) using sleep-breathing data that is significantly more reliable, flexible, and economical than current diagnostic tools. This passive, in-home, contactless monitoring system — resembling a simple home Wi-Fi router — will also enable remote disease assessment and continuous progression tracking. Yang’s groundbreaking work has the potential to advance the diagnosis and treatment of prevalent diseases like PD and AD, and it offers exciting possibilities for addressing many health challenges with reliable, affordable machine-learning tools.  More

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      in Data Management & Statistics

      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