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    This tiny chip can safeguard user data while enabling efficient computing on a smartphone

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    Health-monitoring apps can help people manage chronic diseases or stay on track with fitness goals, using nothing more than a smartphone. However, these apps can be slow and energy-inefficient because the vast machine-learning models that power them must be shuttled between a smartphone and a central memory server.

    Engineers often speed things up using hardware that reduces the need to move so much data back and forth. While these machine-learning accelerators can streamline computation, they are susceptible to attackers who can steal secret information.

    To reduce this vulnerability, researchers from MIT and the MIT-IBM Watson AI Lab created a machine-learning accelerator that is resistant to the two most common types of attacks. Their chip can keep a user’s health records, financial information, or other sensitive data private while still enabling huge AI models to run efficiently on devices.

    The team developed several optimizations that enable strong security while only slightly slowing the device. Moreover, the added security does not impact the accuracy of computations. This machine-learning accelerator could be particularly beneficial for demanding AI applications like augmented and virtual reality or autonomous driving.

    While implementing the chip would make a device slightly more expensive and less energy-efficient, that is sometimes a worthwhile price to pay for security, says lead author Maitreyi Ashok, an electrical engineering and computer science (EECS) graduate student at MIT.

    “It is important to design with security in mind from the ground up. If you are trying to add even a minimal amount of security after a system has been designed, it is prohibitively expensive. We were able to effectively balance a lot of these tradeoffs during the design phase,” says Ashok.

    Her co-authors include Saurav Maji, an EECS graduate student; Xin Zhang and John Cohn of the MIT-IBM Watson AI Lab; and senior author Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of the School of Engineering, and the Vannevar Bush Professor of EECS. The research will be presented at the IEEE Custom Integrated Circuits Conference.

    Side-channel susceptibility

    The researchers targeted a type of machine-learning accelerator called digital in-memory compute. A digital IMC chip performs computations inside a device’s memory, where pieces of a machine-learning model are stored after being moved over from a central server.

    The entire model is too big to store on the device, but by breaking it into pieces and reusing those pieces as much as possible, IMC chips reduce the amount of data that must be moved back and forth.

    But IMC chips can be susceptible to hackers. In a side-channel attack, a hacker monitors the chip’s power consumption and uses statistical techniques to reverse-engineer data as the chip computes. In a bus-probing attack, the hacker can steal bits of the model and dataset by probing the communication between the accelerator and the off-chip memory.

    Digital IMC speeds computation by performing millions of operations at once, but this complexity makes it tough to prevent attacks using traditional security measures, Ashok says.

    She and her collaborators took a three-pronged approach to blocking side-channel and bus-probing attacks.

    First, they employed a security measure where data in the IMC are split into random pieces. For instance, a bit zero might be split into three bits that still equal zero after a logical operation. The IMC never computes with all pieces in the same operation, so a side-channel attack could never reconstruct the real information.

    But for this technique to work, random bits must be added to split the data. Because digital IMC performs millions of operations at once, generating so many random bits would involve too much computing. For their chip, the researchers found a way to simplify computations, making it easier to effectively split data while eliminating the need for random bits.

    Second, they prevented bus-probing attacks using a lightweight cipher that encrypts the model stored in off-chip memory. This lightweight cipher only requires simple computations. In addition, they only decrypted the pieces of the model stored on the chip when necessary.

    Third, to improve security, they generated the key that decrypts the cipher directly on the chip, rather than moving it back and forth with the model. They generated this unique key from random variations in the chip that are introduced during manufacturing, using what is known as a physically unclonable function.

    “Maybe one wire is going to be a little bit thicker than another. We can use these variations to get zeros and ones out of a circuit. For every chip, we can get a random key that should be consistent because these random properties shouldn’t change significantly over time,” Ashok explains.

    They reused the memory cells on the chip, leveraging the imperfections in these cells to generate the key. This requires less computation than generating a key from scratch.

    “As security has become a critical issue in the design of edge devices, there is a need to develop a complete system stack focusing on secure operation. This work focuses on security for machine-learning workloads and describes a digital processor that uses cross-cutting optimization. It incorporates encrypted data access between memory and processor, approaches to preventing side-channel attacks using randomization, and exploiting variability to generate unique codes. Such designs are going to be critical in future mobile devices,” says Chandrakasan.

    Safety testing

    To test their chip, the researchers took on the role of hackers and tried to steal secret information using side-channel and bus-probing attacks.

    Even after making millions of attempts, they couldn’t reconstruct any real information or extract pieces of the model or dataset. The cipher also remained unbreakable. By contrast, it took only about 5,000 samples to steal information from an unprotected chip.

    The addition of security did reduce the energy efficiency of the accelerator, and it also required a larger chip area, which would make it more expensive to fabricate.

    The team is planning to explore methods that could reduce the energy consumption and size of their chip in the future, which would make it easier to implement at scale.

    “As it becomes too expensive, it becomes harder to convince someone that security is critical. Future work could explore these tradeoffs. Maybe we could make it a little less secure but easier to implement and less expensive,” Ashok says.

    The research is funded, in part, by the MIT-IBM Watson AI Lab, the National Science Foundation, and a Mathworks Engineering Fellowship. 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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      Improving drug development with a vast map of the immune system

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      The human immune system is a network made up of trillions of cells that are constantly circulating throughout the body. The cellular network orchestrates interactions with every organ and tissue to carry out an impossibly long list of functions that scientists are still working to understand. All that complexity limits our ability to predict which patients will respond to treatments and which ones might suffer debilitating side effects.

      The issue often leads pharmaceutical companies to stop developing drugs that could help certain patients, halting clinical trials even when drugs show promising results for some people.

      Now, Immunai is helping to predict how patients will respond to treatments by building a comprehensive map of the immune system. The company has assembled a vast database it calls AMICA, that combines multiple layers of gene and protein expression data in cells with clinical trial data to match the right drugs to the right patients.

      “Our starting point was creating what I call the Google Maps for the immune system,” Immunai co-founder and CEO Noam Solomon says. “We started with single-cell RNA sequencing, and over time we’ve added more and more ‘omics’: genomics, proteomics, epigenomics, all to measure the immune system’s cellular expression and function, to measure the immune environment holistically. Then we started working with pharmaceutical companies and hospitals to profile the immune systems of patients undergoing treatments to really get to the root mechanisms of action and resistance for therapeutics.”

      Immunai’s big data foundation is a result of its founders’ unique background. Solomon and co-founder Luis Voloch ’13, SM ’15 hold degrees in mathematics and computer science. In fact, Solomon was a postdoc in MIT’s Department of Mathematics at the time of Immunai’s founding.

      Solomon frames Immunai’s mission as stopping the decades-long divergence of computer science and the life sciences. He believes the single biggest factor driving the explosion of computing has been Moore’s Law — our ability to exponentially increase the number of transistors on a chip over the past 60 years. In the pharmaceutical industry, the reverse is happening: By one estimate, the cost of developing a new drug roughly doubles every nine years. The phenomenon has been dubbed Eroom’s Law (“Eroom” for “Moore” spelled backward).

      Solomon sees the trend eroding the case for developing new drugs, with huge consequences for patients.

      “Why should pharmaceutical companies invest in discovery if they won’t get a return on investment?” Solomon asks. “Today, there’s only a 5 to 10 percent chance that any given clinical trial will be successful. What we’ve built through a very robust and granular mapping of the immune system is a chance to improve the preclinical and clinical stages of drug development.”

      A change in plans

      Solomon entered Tel Aviv University when he was 14 and earned his bachelor’s degree in computer science by 19. He earned two PhDs in Israel, one in computer science and the other in mathematics, before coming to MIT in 2017 as a postdoc to continue his mathematical research career.

      That year Solomon met Voloch, who had already earned bachelor’s and master’s degrees in math and computer science from MIT. But the researchers were soon exposed to a problem that would take them out of their comfort zones and change the course of their careers.

      Voloch’s grandfather was receiving a cocktail of treatments for cancer at the time. The cancer went into remission, but he suffered terrible side effects that caused him to stop taking his medication.

      Voloch and Solomon began wondering if their expertise could help patients like Voloch’s grandfather.

      “When we realized we could make an impact, we made the difficult decision to stop our academic pursuits and start a new journey,” Solomon recalls. “That was the starting point for Immunai.”

      Voloch and Solomon soon partnered with Immunai scientific co-founders Ansu Satpathy, a researcher at Stanford University at the time, and Danny Wells, a researcher at the Parker Institute for Cancer Immunotherapy. Satpathy and Wells had shown that single-cell RNA sequencing could be used to gain insights into why patients respond differently to a common cancer treatment.

      The team began analyzing single-cell RNA sequencing data published in scientific papers, trying to link common biomarkers with patient outcomes. Then they integrated data from the United Kingdom’s Biobank public health database, finding they were able to improve their models’ predictions. Soon they were incorporating data from hospitals, academic research institutions, and pharmaceutical companies, analyzing information about the structure, function, and environment of cells — multiomics — to get a clearer picture of immune activity.

      “Single cell sequencing gives you metrics you can measure in thousands of cells, where you can look at 20,000 different genes, and those metrics give you an immune profile,” Solomon explains. “When you measure all of that over time, especially before and after getting therapy, and compare patients who do respond with patients who don’t, you can apply machine learning models to understand why.”

      Those data and models make up AMICA, what Immunai calls the world’s largest cell-level immune knowledge base. AMICA stands for Annotated Multiomic Immune Cell Atlas. It analyzes single cell multiomic data from almost 10,000 patients and bulk-RNA data from 100,000 patients across more than 800 cell types and 500 diseases.

      At the core of Immunai’s approach is a focus on the immune system, which other companies shy away from because of its complexity.

      “We don’t want to be like other groups that are studying mainly tumor microenvironments,” Solomon says. “We look at the immune system because the immune system is the common denominator. It’s the one system that is implicated in every disease, in your body’s response to everything that you encounter, whether it’s a viral infection or bacterial infection or a drug that you are receiving — even how you are aging.”

      Turning data into better treatments

      Immunai has already partnered with some of the largest pharmaceutical companies in the world to help them identify promising treatments and set up their clinical trials for success. Immunai’s insights can help partners make critical decisions about treatment schedules, dosing, drug combinations, patient selection, and more.

      “Everyone is talking about AI, but I think the most exciting aspect of the platform we have built is the fact that it’s vertically integrated, from wet lab to computational modeling with multiple iterations,” Solomon says. “For example, we may do single-cell immune profiling of patient samples, then we upload that data to the cloud and our computational models come up with insights, and with those insights we do in vitro or in vivo validation to see if our models are right and iteratively improve them.”

      Ultimately Immunai wants to enable a future where lab experiments can more reliably turn into impactful new recommendations and treatments for patients.

      “Scientists can cure nearly every type of cancer, but only in mice,” Solomon says. “In preclinical models we know how to cure cancer. In human beings, in most cases, we still don’t. To overcome that, most scientists are looking for better ex vivo or in vivo models. Our approach is to be more agnostic as to the model system, but feed the machine with more and more data from multiple model systems. We’re demonstrating that our algorithms can repeatedly beat the top benchmarks in identifying the top preclinical immune features that match to patient outcomes.” More

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      Characterizing social networks

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      People tend to connect with others who are like them. Alumni from the same alma mater are more likely to collaborate over a research project together, or individuals with the same political beliefs are more likely to join the same political parties, attend rallies, and engage in online discussions. This sociology concept, called homophily, has been observed in many network science studies. But if like-minded individuals cluster in online and offline spaces to reinforce each other’s ideas and form synergies, what does that mean for society?

      Researchers at MIT wanted to investigate homophily further to understand how groups of three or more interact in complex societal settings. Prior research on understanding homophily has studied relationships between pairs of people. For example, when two members of Congress co-sponsor a bill, they are likely to be from the same political party.

      However, less is known about whether group interactions between three or more people are likely to occur between similar individuals. If three members of Congress co-sponsor a bill together, are all three likely to be members of the same party, or would we expect more bipartisanship? When the researchers tried to extend traditional methods to measure homophily in these larger group interactions, they found the results can be misleading.

      “We found that homophily observed in pairs, or one-to-one interactions, can make it seem like there’s more homophily in larger groups than there really is,” says Arnab Sarker, graduate student in the Institute for Data, Systems and Society (IDSS) and lead author of the study published in Proceedings of the National Academy of Sciences. “The previous measure didn’t account for the way in which two people already know each other in friendship settings,” he adds.

      To address this issue, Sarker, along with co-authors Natalie Northrup ’22 and Ali Jadbabaie, the JR East Professor of Engineering, head of the Department of Civil and Environmental Engineering, and core faculty member of IDSS, developed a new way of measuring homophily. Borrowing tools from algebraic topology, a subfield in mathematics typically applied in physics, they developed a new measure to understand whether homophily occurred in group interactions.

      The new measure, called simplicial homophily, separates the homophily seen in one-on-one interactions from those in larger group interactions and is based on the mathematical concept of a simplicial complex. The researchers tested this new measure with real-world data from 16 different datasets and found that simplicial homophily provides more accurate insights into how similar things interact in larger groups. Interestingly, the new measure can better identify instances where there is a lack of similarity in larger group interactions, thus rectifying a weakness observed in the previous measure.

      One such example of this instance was demonstrated in the dataset from the global hotel booking website, Trivago. They found that when travelers are looking at two hotels in one session, they often pick hotels that are close to one another geographically. But when they look at more than two hotels in one session, they are more likely to be searching for hotels that are farther apart from one another (for example, if they are taking a vacation with multiple stops). The new method showed “anti-homophily” — instead of similar hotels being chosen together, different hotels were chosen together.

      “Our measure controls for pairwise connections and is suggesting that there’s more diversity in the hotels that people are looking for as group size increases, which is an interesting economic result,” says Sarker.

      Additionally, they discovered that simplicial homophily can help identify when certain characteristics are important for predicting if groups will interact in the future. They found that when there’s a lot of similarity or a lot of difference between individuals who already interact in groups, then knowing individual characteristics can help predict their connection to each other in the future.

      Northrup was an undergraduate researcher on the project and worked with Sarker and Jadbabaie over three semesters before she graduated. The project gave her an opportunity to take some of the concepts she learned in the classroom and apply them.

      “Working on this project, I really dove into building out the higher-order network model, and understanding the network, the math, and being able to implement it at a large scale,” says Northrup, who was in the civil and environmental engineering systems track with a double major in economics.

      The new measure opens up opportunities to study complex group interactions in a broad range of network applications, from ecology to traffic and socioeconomics. One of the areas Sarker has interest in exploring is the group dynamics of people finding jobs through social networks. “Does higher-order homophily affect how people get information about jobs?” he asks.    

      Northrup adds that it could also be used to evaluate interventions or specific policies to connect people with job opportunities outside of their network. “You can even use it as a measurement to evaluate how effective that might be.”

      The research was supported through funding from a Vannevar Bush Fellowship from the Office of the U.S. Secretary of Defense and from the U.S. Army Research Office Multidisciplinary University Research Initiative. More

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      New software enables blind and low-vision users to create interactive, accessible charts

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      A growing number of tools enable users to make online data representations, like charts, that are accessible for people who are blind or have low vision. However, most tools require an existing visual chart that can then be converted into an accessible format.

      This creates barriers that prevent blind and low-vision users from building their own custom data representations, and it can limit their ability to explore and analyze important information.

      A team of researchers from MIT and University College London (UCL) wants to change the way people think about accessible data representations.

      They created a software system called Umwelt (which means “environment” in German) that can enable blind and low-vision users to build customized, multimodal data representations without needing an initial visual chart.

      Umwelt, an authoring environment designed for screen-reader users, incorporates an editor that allows someone to upload a dataset and create a customized representation, such as a scatterplot, that can include three modalities: visualization, textual description, and sonification. Sonification involves converting data into nonspeech audio.

      The system, which can represent a variety of data types, includes a viewer that enables a blind or low-vision user to interactively explore a data representation, seamlessly switching between each modality to interact with data in a different way.

      The researchers conducted a study with five expert screen-reader users who found Umwelt to be useful and easy to learn. In addition to offering an interface that empowered them to create data representations — something they said was sorely lacking — the users said Umwelt could facilitate communication between people who rely on different senses.

      “We have to remember that blind and low-vision people aren’t isolated. They exist in these contexts where they want to talk to other people about data,” says Jonathan Zong, an electrical engineering and computer science (EECS) graduate student and lead author of a paper introducing Umwelt. “I am hopeful that Umwelt helps shift the way that researchers think about accessible data analysis. Enabling the full participation of blind and low-vision people in data analysis involves seeing visualization as just one piece of this bigger, multisensory puzzle.”

      Joining Zong on the paper are fellow EECS graduate students Isabella Pedraza Pineros and Mengzhu “Katie” Chen; Daniel Hajas, a UCL researcher who works with the Global Disability Innovation Hub; and senior author Arvind Satyanarayan, associate professor of computer science at MIT who leads the Visualization Group in the Computer Science and Artificial Intelligence Laboratory. The paper will be presented at the ACM Conference on Human Factors in Computing.

      De-centering visualization

      The researchers previously developed interactive interfaces that provide a richer experience for screen reader users as they explore accessible data representations. Through that work, they realized most tools for creating such representations involve converting existing visual charts.

      Aiming to decenter visual representations in data analysis, Zong and Hajas, who lost his sight at age 16, began co-designing Umwelt more than a year ago.

      At the outset, they realized they would need to rethink how to represent the same data using visual, auditory, and textual forms.

      “We had to put a common denominator behind the three modalities. By creating this new language for representations, and making the output and input accessible, the whole is greater than the sum of its parts,” says Hajas.

      To build Umwelt, they first considered what is unique about the way people use each sense.

      For instance, a sighted user can see the overall pattern of a scatterplot and, at the same time, move their eyes to focus on different data points. But for someone listening to a sonification, the experience is linear since data are converted into tones that must be played back one at a time.

      “If you are only thinking about directly translating visual features into nonvisual features, then you miss out on the unique strengths and weaknesses of each modality,” Zong adds.

      They designed Umwelt to offer flexibility, enabling a user to switch between modalities easily when one would better suit their task at a given time.

      To use the editor, one uploads a dataset to Umwelt, which employs heuristics to automatically creates default representations in each modality.

      If the dataset contains stock prices for companies, Umwelt might generate a multiseries line chart, a textual structure that groups data by ticker symbol and date, and a sonification that uses tone length to represent the price for each date, arranged by ticker symbol.

      The default heuristics are intended to help the user get started.

      “In any kind of creative tool, you have a blank-slate effect where it is hard to know how to begin. That is compounded in a multimodal tool because you have to specify things in three different representations,” Zong says.

      The editor links interactions across modalities, so if a user changes the textual description, that information is adjusted in the corresponding sonification. Someone could utilize the editor to build a multimodal representation, switch to the viewer for an initial exploration, then return to the editor to make adjustments.

      Helping users communicate about data

      To test Umwelt, they created a diverse set of multimodal representations, from scatterplots to multiview charts, to ensure the system could effectively represent different data types. Then they put the tool in the hands of five expert screen reader users.

      Study participants mostly found Umwelt to be useful for creating, exploring, and discussing data representations. One user said Umwelt was like an “enabler” that decreased the time it took them to analyze data. The users agreed that Umwelt could help them communicate about data more easily with sighted colleagues.

      “What stands out about Umwelt is its core philosophy of de-emphasizing the visual in favor of a balanced, multisensory data experience. Often, nonvisual data representations are relegated to the status of secondary considerations, mere add-ons to their visual counterparts. However, visualization is merely one aspect of data representation. I appreciate their efforts in shifting this perception and embracing a more inclusive approach to data science,” says JooYoung Seo, an assistant professor in the School of Information Sciences at the University of Illinois at Urbana-Champagne, who was not involved with this work.

      Moving forward, the researchers plan to create an open-source version of Umwelt that others can build upon. They also want to integrate tactile sensing into the software system as an additional modality, enabling the use of tools like refreshable tactile graphics displays.

      “In addition to its impact on end users, I am hoping that Umwelt can be a platform for asking scientific questions around how people use and perceive multimodal representations, and how we can improve the design beyond this initial step,” says Zong.

      This work was supported, in part, by the National Science Foundation and the MIT Morningside Academy for Design Fellowship. More

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      Q&A: How refusal can be an act of design

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      This month in the ACM Journal on Responsible Computing, MIT graduate student Jonathan Zong SM ’20 and co-author J. Nathan Matias SM ’13, PhD ’17 of the Cornell Citizens and Technology Lab examine how the notion of refusal can open new avenues in the field of data ethics. In their open-access report, “Data Refusal From Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design,” the pair proposes a framework in four dimensions to map how individuals can say “no” to technology misuses. At the same time, the researchers argue that just like design, refusal is generative, and has the potential to create alternate futures.

      Zong, a PhD candidate in electrical engineering and computer science, 2022-23 MIT Morningside Academy for Design Design Fellow, and member of the MIT Visualization Group, describes his latest work in this Q&A.

      Q: How do you define the concept of “refusal,” and where does it come from?

      A: Refusal was developed in feminist and Indigenous studies. It’s this idea of saying “no,” without being given permission to say “no.” Scholars like Ruha Benjamin write about refusal in the context of surveillance, race, and bioethics, and talk about it as a necessary counterpart to consent. Others, like the authors of the “Feminist Data Manifest-No,” think of refusal as something that can help us commit to building better futures.

      Benjamin illustrates cases where the choice to refuse is not equally possible for everyone, citing examples involving genetic data and refugee screenings in the U.K. The imbalance of power in these situations underscores the broader concept of refusal, extending beyond rejecting specific options to challenging the entire set of choices presented.

      Q: What inspired you to work on the notion of refusal as an act of design?

      A: In my work on data ethics, I’ve been thinking about how to incorporate processes into research data collection, particularly around consent and opt-out, with a focus on individual autonomy and the idea of giving people choices about the way that their data is used. But when it comes to data privacy, simply making choices available is not enough. Choices can be unequally available, or create no-win situations where all options are bad. This led me to the concept of refusal: questioning the authority of data collectors and challenging their legitimacy.

      The key idea of my work is that refusal is an act of design. I think of refusal as deliberate actions to redesign our socio-technical landscape by exerting some sort of influence. Like design, refusal is generative. Like design, it’s oriented towards creating alternate possibilities and alternate futures. Design is a process of exploring or traversing a space of possibility. Applying a design framework to cases of refusal drawn from scholarly and journalistic sources allowed me to establish a common language for talking about refusal and to imagine refusals that haven’t been explored yet.

      Q: What are the stakes around data privacy and data collection?

      A: The use of data for facial recognition surveillance in the U.S. is a big example we use in the paper. When people do everyday things like post on social media or walk past cameras in public spaces, they might be contributing their data to training facial recognition systems. For instance, a tech company may take photos from a social media site and build facial recognition that they then sell to the government. In the U.S., these systems are disproportionately used by police to surveil communities of color. It is difficult to apply concepts like consent and opt out of these processes, because they happen over time and involve multiple kinds of institutions. It’s also not clear that individual opt-out would do anything to change the overall situation. Refusal then becomes a crucial avenue, at both individual and community levels, to think more broadly of how affected people still exert some kind of voice or agency, without necessarily having an official channel to do so.

      Q: Why do you think these issues are more particularly affecting disempowered communities?

      A: People who are affected by technologies are not always included in the design process for those technologies. Refusal then becomes a meaningful expression of values and priorities for those who were not part of the early design conversations. Actions taken against technologies like face surveillance — be it legal battles against companies, advocacy for stricter regulations, or even direct action like disabling security cameras — may not fit the conventional notion of participating in a design process. And yet, these are the actions available to refusers who may be excluded from other forms of participation.

      I’m particularly inspired by the movement around Indigenous data sovereignty. Organizations like the First Nations Information Governance Centre work towards prioritizing Indigenous communities’ perspectives in data collection, and refuse inadequate representation in official health data from the Canadian government. I think this is a movement that exemplifies the potential of refusal, not only as a way to reject what’s being offered, but also as a means to propose a constructive alternative, very much like design. Refusal is not merely a negation, but a pathway to different futures.

      Q: Can you elaborate on the design framework you propose?

      A: Refusals vary widely across contexts and scales. Developing a framework for refusal is about helping people see actions that are seemingly very different as instances of the same broader idea. Our framework consists of four facets: autonomy, time, power, and cost.

      Consider the case of IBM creating a facial recognition dataset using people’s photos without consent. We saw multiple forms of refusal emerge in response. IBM allowed individuals to opt out by withdrawing their photos. People collectively refused by creating a class-action lawsuit against IBM. Around the same time, many U.S. cities started passing local legislation banning the government use of facial recognition. Evaluating these cases through the framework highlights commonalities and differences. The framework highlights varied approaches to autonomy, like individual opt-out and collective action. Regarding time, opt-outs and lawsuits react to past harm, while legislation might proactively prevent future harm. Power dynamics differ; withdrawing individual photos minimally influences IBM, while legislation could potentially cause longer-term change. And as for cost, individual opt-out seems less demanding, while other approaches require more time and effort, balanced against potential benefits.

      The framework facilitates case description and comparison across these dimensions. I think its generative nature encourages exploration of novel forms of refusal as well. By identifying the characteristics we want to see in future refusal strategies — collective, proactive, powerful, low-cost… — we can aspire to shape future approaches and change the behavior of data collectors. We may not always be able to combine all these criteria, but the framework provides a means to articulate our aspirational goals in this context.

      Q: What impact do you hope this research will have?

      A: I hope to expand the notion of who can participate in design, and whose actions are seen as legitimate expressions of design input. I think a lot of work so far in the conversation around data ethics prioritizes the perspective of computer scientists who are trying to design better systems, at the expense of the perspective of people for whom the systems are not currently working. So, I hope designers and computer scientists can embrace the concept of refusal as a legitimate form of design, and a source of inspiration. There’s a vital conversation happening, one that should influence the design of future systems, even if expressed through unconventional means.

      One of the things I want to underscore in the paper is that design extends beyond software. Taking a socio-technical perspective, the act of designing encompasses software, institutions, relationships, and governance structures surrounding data use. I want people who aren’t software engineers, like policymakers or activists, to view themselves as integral to the technology design process. More

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      AI generates high-quality images 30 times faster in a single step

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      In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges. Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image.

      MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have introduced a new framework that simplifies the multi-step process of traditional diffusion models into a single step, addressing previous limitations. This is done through a type of teacher-student model: teaching a new computer model to mimic the behavior of more complicated, original models that generate images. The approach, known as distribution matching distillation (DMD), retains the quality of the generated images and allows for much faster generation. 

      “Our work is a novel method that accelerates current diffusion models such as Stable Diffusion and DALLE-3 by 30 times,” says Tianwei Yin, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and the lead researcher on the DMD framework. “This advancement not only significantly reduces computational time but also retains, if not surpasses, the quality of the generated visual content. Theoretically, the approach marries the principles of generative adversarial networks (GANs) with those of diffusion models, achieving visual content generation in a single step — a stark contrast to the hundred steps of iterative refinement required by current diffusion models. It could potentially be a new generative modeling method that excels in speed and quality.”

      This single-step diffusion model could enhance design tools, enabling quicker content creation and potentially supporting advancements in drug discovery and 3D modeling, where promptness and efficacy are key.

      Distribution dreams

      DMD cleverly has two components. First, it uses a regression loss, which anchors the mapping to ensure a coarse organization of the space of images to make training more stable. Next, it uses a distribution matching loss, which ensures that the probability to generate a given image with the student model corresponds to its real-world occurrence frequency. To do this, it leverages two diffusion models that act as guides, helping the system understand the difference between real and generated images and making training the speedy one-step generator possible.

      The system achieves faster generation by training a new network to minimize the distribution divergence between its generated images and those from the training dataset used by traditional diffusion models. “Our key insight is to approximate gradients that guide the improvement of the new model using two diffusion models,” says Yin. “In this way, we distill the knowledge of the original, more complex model into the simpler, faster one, while bypassing the notorious instability and mode collapse issues in GANs.” 

      Yin and colleagues used pre-trained networks for the new student model, simplifying the process. By copying and fine-tuning parameters from the original models, the team achieved fast training convergence of the new model, which is capable of producing high-quality images with the same architectural foundation. “This enables combining with other system optimizations based on the original architecture to further accelerate the creation process,” adds Yin. 

      When put to the test against the usual methods, using a wide range of benchmarks, DMD showed consistent performance. On the popular benchmark of generating images based on specific classes on ImageNet, DMD is the first one-step diffusion technique that churns out pictures pretty much on par with those from the original, more complex models, rocking a super-close Fréchet inception distance (FID) score of just 0.3, which is impressive, since FID is all about judging the quality and diversity of generated images. Furthermore, DMD excels in industrial-scale text-to-image generation and achieves state-of-the-art one-step generation performance. There’s still a slight quality gap when tackling trickier text-to-image applications, suggesting there’s a bit of room for improvement down the line. 

      Additionally, the performance of the DMD-generated images is intrinsically linked to the capabilities of the teacher model used during the distillation process. In the current form, which uses Stable Diffusion v1.5 as the teacher model, the student inherits limitations such as rendering detailed depictions of text and small faces, suggesting that DMD-generated images could be further enhanced by more advanced teacher models. 

      “Decreasing the number of iterations has been the Holy Grail in diffusion models since their inception,” says Fredo Durand, MIT professor of electrical engineering and computer science, CSAIL principal investigator, and a lead author on the paper. “We are very excited to finally enable single-step image generation, which will dramatically reduce compute costs and accelerate the process.” 

      “Finally, a paper that successfully combines the versatility and high visual quality of diffusion models with the real-time performance of GANs,” says Alexei Efros, a professor of electrical engineering and computer science at the University of California at Berkeley who was not involved in this study. “I expect this work to open up fantastic possibilities for high-quality real-time visual editing.” 

      Yin and Durand’s fellow authors are MIT electrical engineering and computer science professor and CSAIL principal investigator William T. Freeman, as well as Adobe research scientists Michaël Gharbi SM ’15, PhD ’18; Richard Zhang; Eli Shechtman; and Taesung Park. Their work was supported, in part, by U.S. National Science Foundation grants (including one for the Institute for Artificial Intelligence and Fundamental Interactions), the Singapore Defense Science and Technology Agency, and by funding from Gwangju Institute of Science and Technology and Amazon. Their work will be presented at the Conference on Computer Vision and Pattern Recognition in June. More

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      Using generative AI to improve software testing

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      Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.

      Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive.

      For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.

      The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders — Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki ’15, SM ’16 — believe the company’s success is due to SDV’s ability to revolutionize software testing.

      SDV goes viral

      In 2016, Veeramachaneni’s group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.

      Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.

      Veeramachaneni’s group came across the problem because it was working with companies that wanted to share their data for research.

      “MIT helps you see all these different use cases,” Patki explains. “You work with finance companies and health care companies, and all those projects are useful to formulate solutions across industries.”

      In 2020, the researchers founded DataCebo to build more SDV features for larger organizations. Since then, the use cases have been as impressive as they’ve been varied.

      With DataCebo’s new flight simulator, for instance, airlines can plan for rare weather events in a way that would be impossible using only historic data. In another application, SDV users synthesized medical records to predict health outcomes for patients with cystic fibrosis. A team from Norway recently used SDV to create synthetic student data to evaluate whether various admissions policies were meritocratic and free from bias.

      In 2021, the data science platform Kaggle hosted a competition for data scientists that used SDV to create synthetic data sets to avoid using proprietary data. Roughly 30,000 data scientists participated, building solutions and predicting outcomes based on the company’s realistic data.

      And as DataCebo has grown, it’s stayed true to its MIT roots: All of the company’s current employees are MIT alumni.

      Supercharging software testing

      Although their open-source tools are being used for a variety of use cases, the company is focused on growing its traction in software testing.

      “You need data to test these software applications,” Veeramachaneni says. “Traditionally, developers manually write scripts to create synthetic data. With generative models, created using SDV, you can learn from a sample of data collected and then sample a large volume of synthetic data (which has the same properties as real data), or create specific scenarios and edge cases, and use the data to test your application.”

      For example, if a bank wanted to test a program designed to reject transfers from accounts with no money in them, it would have to simulate many accounts simultaneously transacting. Doing that with data created manually would take a lot of time. With DataCebo’s generative models, customers can create any edge case they want to test.

      “It’s common for industries to have data that is sensitive in some capacity,” Patki says. “Often when you’re in a domain with sensitive data you’re dealing with regulations, and even if there aren’t legal regulations, it’s in companies’ best interest to be diligent about who gets access to what at which time. So, synthetic data is always better from a privacy perspective.”

      Scaling synthetic data

      Veeramachaneni believes DataCebo is advancing the field of what it calls synthetic enterprise data, or data generated from user behavior on large companies’ software applications.

      “Enterprise data of this kind is complex, and there is no universal availability of it, unlike language data,” Veeramachaneni says. “When folks use our publicly available software and report back if works on a certain pattern, we learn a lot of these unique patterns, and it allows us to improve our algorithms. From one perspective, we are building a corpus of these complex patterns, which for language and images is readily available. “

      DataCebo also recently released features to improve SDV’s usefulness, including tools to assess the “realism” of the generated data, called the SDMetrics library as well as a way to compare models’ performances called SDGym.

      “It’s about ensuring organizations trust this new data,” Veeramachaneni says. “[Our tools offer] programmable synthetic data, which means we allow enterprises to insert their specific insight and intuition to build more transparent models.”

      As companies in every industry rush to adopt AI and other data science tools, DataCebo is ultimately helping them do so in a way that is more transparent and responsible.

      “In the next few years, synthetic data from generative models will transform all data work,” Veeramachaneni says. “We believe 90 percent of enterprise operations can be done with synthetic data.” More

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      Dealing with the limitations of our noisy world

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      Tamara Broderick first set foot on MIT’s campus when she was a high school student, as a participant in the inaugural Women’s Technology Program. The monthlong summer academic experience gives young women a hands-on introduction to engineering and computer science.

      What is the probability that she would return to MIT years later, this time as a faculty member?

      That’s a question Broderick could probably answer quantitatively using Bayesian inference, a statistical approach to probability that tries to quantify uncertainty by continuously updating one’s assumptions as new data are obtained.

      In her lab at MIT, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS) uses Bayesian inference to quantify uncertainty and measure the robustness of data analysis techniques.

      “I’ve always been really interested in understanding not just ‘What do we know from data analysis,’ but ‘How well do we know it?’” says Broderick, who is also a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society. “The reality is that we live in a noisy world, and we can’t always get exactly the data that we want. How do we learn from data but at the same time recognize that there are limitations and deal appropriately with them?”

      Broadly, her focus is on helping people understand the confines of the statistical tools available to them and, sometimes, working with them to craft better tools for a particular situation.

      For instance, her group recently collaborated with oceanographers to develop a machine-learning model that can make more accurate predictions about ocean currents. In another project, she and others worked with degenerative disease specialists on a tool that helps severely motor-impaired individuals utilize a computer’s graphical user interface by manipulating a single switch.

      A common thread woven through her work is an emphasis on collaboration.

      “Working in data analysis, you get to hang out in everybody’s backyard, so to speak. You really can’t get bored because you can always be learning about some other field and thinking about how we can apply machine learning there,” she says.

      Hanging out in many academic “backyards” is especially appealing to Broderick, who struggled even from a young age to narrow down her interests.

      A math mindset

      Growing up in a suburb of Cleveland, Ohio, Broderick had an interest in math for as long as she can remember. She recalls being fascinated by the idea of what would happen if you kept adding a number to itself, starting with 1+1=2 and then 2+2=4.

      “I was maybe 5 years old, so I didn’t know what ‘powers of two’ were or anything like that. I was just really into math,” she says.

      Her father recognized her interest in the subject and enrolled her in a Johns Hopkins program called the Center for Talented Youth, which gave Broderick the opportunity to take three-week summer classes on a range of subjects, from astronomy to number theory to computer science.

      Later, in high school, she conducted astrophysics research with a postdoc at Case Western University. In the summer of 2002, she spent four weeks at MIT as a member of the first class of the Women’s Technology Program.

      She especially enjoyed the freedom offered by the program, and its focus on using intuition and ingenuity to achieve high-level goals. For instance, the cohort was tasked with building a device with LEGOs that they could use to biopsy a grape suspended in Jell-O.

      The program showed her how much creativity is involved in engineering and computer science, and piqued her interest in pursuing an academic career.

      “But when I got into college at Princeton, I could not decide — math, physics, computer science — they all seemed super-cool. I wanted to do all of it,” she says.

      She settled on pursuing an undergraduate math degree but took all the physics and computer science courses she could cram into her schedule.

      Digging into data analysis

      After receiving a Marshall Scholarship, Broderick spent two years at Cambridge University in the United Kingdom, earning a master of advanced study in mathematics and a master of philosophy in physics.

      In the UK, she took a number of statistics and data analysis classes, including her first class on Bayesian data analysis in the field of machine learning.

      It was a transformative experience, she recalls.

      “During my time in the U.K., I realized that I really like solving real-world problems that matter to people, and Bayesian inference was being used in some of the most important problems out there,” she says.

      Back in the U.S., Broderick headed to the University of California at Berkeley, where she joined the lab of Professor Michael I. Jordan as a grad student. She earned a PhD in statistics with a focus on Bayesian data analysis. 

      She decided to pursue a career in academia and was drawn to MIT by the collaborative nature of the EECS department and by how passionate and friendly her would-be colleagues were.

      Her first impressions panned out, and Broderick says she has found a community at MIT that helps her be creative and explore hard, impactful problems with wide-ranging applications.

      “I’ve been lucky to work with a really amazing set of students and postdocs in my lab — brilliant and hard-working people whose hearts are in the right place,” she says.

      One of her team’s recent projects involves a collaboration with an economist who studies the use of microcredit, or the lending of small amounts of money at very low interest rates, in impoverished areas.

      The goal of microcredit programs is to raise people out of poverty. Economists run randomized control trials of villages in a region that receive or don’t receive microcredit. They want to generalize the study results, predicting the expected outcome if one applies microcredit to other villages outside of their study.

      But Broderick and her collaborators have found that results of some microcredit studies can be very brittle. Removing one or a few data points from the dataset can completely change the results. One issue is that researchers often use empirical averages, where a few very high or low data points can skew the results.

      Using machine learning, she and her collaborators developed a method that can determine how many data points must be dropped to change the substantive conclusion of the study. With their tool, a scientist can see how brittle the results are.

      “Sometimes dropping a very small fraction of data can change the major results of a data analysis, and then we might worry how far those conclusions generalize to new scenarios. Are there ways we can flag that for people? That is what we are getting at with this work,” she explains.

      At the same time, she is continuing to collaborate with researchers in a range of fields, such as genetics, to understand the pros and cons of different machine-learning techniques and other data analysis tools.

      Happy trails

      Exploration is what drives Broderick as a researcher, and it also fuels one of her passions outside the lab. She and her husband enjoy collecting patches they earn by hiking all the trails in a park or trail system.

      “I think my hobby really combines my interests of being outdoors and spreadsheets,” she says. “With these hiking patches, you have to explore everything and then you see areas you wouldn’t normally see. It is adventurous, in that way.”

      They’ve discovered some amazing hikes they would never have known about, but also embarked on more than a few “total disaster hikes,” she says. But each hike, whether a hidden gem or an overgrown mess, offers its own rewards.

      And just like in her research, curiosity, open-mindedness, and a passion for problem-solving have never led her astray. More