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    Fostering research, careers, and community in materials science

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    Gabrielle Wood, a junior at Howard University majoring in chemical engineering, is on a mission to improve the sustainability and life cycles of natural resources and materials. Her work in the Materials Initiative for Comprehensive Research Opportunity (MICRO) program has given her hands-on experience with many different aspects of research, including MATLAB programming, experimental design, data analysis, figure-making, and scientific writing.Wood is also one of 10 undergraduates from 10 universities around the United States to participate in the first MICRO Summit earlier this year. The internship program, developed by the MIT Department of Materials Science and Engineering (DMSE), first launched in fall 2021. Now in its third year, the program continues to grow, providing even more opportunities for non-MIT undergraduate students — including the MICRO Summit and the program’s expansion to include Northwestern University.“I think one of the most valuable aspects of the MICRO program is the ability to do research long term with an experienced professor in materials science and engineering,” says Wood. “My school has limited opportunities for undergraduate research in sustainable polymers, so the MICRO program allowed me to gain valuable experience in this field, which I would not otherwise have.”Like Wood, Griheydi Garcia, a senior chemistry major at Manhattan College, values the exposure to materials science, especially since she is not able to learn as much about it at her home institution.“I learned a lot about crystallography and defects in materials through the MICRO curriculum, especially through videos,” says Garcia. “The research itself is very valuable, as well, because we get to apply what we’ve learned through the videos in the research we do remotely.”Expanding research opportunitiesFrom the beginning, the MICRO program was designed as a fully remote, rigorous education and mentoring program targeted toward students from underserved backgrounds interested in pursuing graduate school in materials science or related fields. Interns are matched with faculty to work on their specific research interests.Jessica Sandland ’99, PhD ’05, principal lecturer in DMSE and co-founder of MICRO, says that research projects for the interns are designed to be work that they can do remotely, such as developing a machine-learning algorithm or a data analysis approach.“It’s important to note that it’s not just about what the program and faculty are bringing to the student interns,” says Sandland, a member of the MIT Digital Learning Lab, a joint program between MIT Open Learning and the Institute’s academic departments. “The students are doing real research and work, and creating things of real value. It’s very much an exchange.”Cécile Chazot PhD ’22, now an assistant professor of materials science and engineering at Northwestern University, had helped to establish MICRO at MIT from the very beginning. Once at Northwestern, she quickly realized that expanding MICRO to Northwestern would offer even more research opportunities to interns than by relying on MIT alone — leveraging the university’s strong materials science and engineering department, as well as offering resources for biomaterials research through Northwestern’s medical school. The program received funding from 3M and officially launched at Northwestern in fall 2023. Approximately half of the MICRO interns are now in the program with MIT and half are with Northwestern. Wood and Garcia both participate in the program via Northwestern.“By expanding to another school, we’ve been able to have interns work with a much broader range of research projects,” says Chazot. “It has become easier for us to place students with faculty and research that match their interests.”Building communityThe MICRO program received a Higher Education Innovation grant from the Abdul Latif Jameel World Education Lab, part of MIT Open Learning, to develop an in-person summit. In January 2024, interns visited MIT for three days of presentations, workshops, and campus tours — including a tour of the MIT.nano building — as well as various community-building activities.“A big part of MICRO is the community,” says Chazot. “A highlight of the summit was just seeing the students come together.”The summit also included panel discussions that allowed interns to gain insights and advice from graduate students and professionals. The graduate panel discussion included MIT graduate students Sam Figueroa (mechanical engineering), Isabella Caruso (DMSE), and Eliana Feygin (DMSE). The career panel was led by Chazot and included Jatin Patil PhD ’23, head of product at SiTration; Maureen Reitman ’90, ScD ’93, group vice president and principal engineer at Exponent; Lucas Caretta PhD ’19, assistant professor of engineering at Brown University; Raquel D’Oyen ’90, who holds a PhD from Northwestern University and is a senior engineer at Raytheon; and Ashley Kaiser MS ’19, PhD ’21, senior process engineer at 6K.Students also had an opportunity to share their work with each other through research presentations. Their presentations covered a wide range of topics, including: developing a computer program to calculate solubility parameters for polymers used in textile manufacturing; performing a life-cycle analysis of a photonic chip and evaluating its environmental impact in comparison to a standard silicon microchip; and applying machine learning algorithms to scanning transmission electron microscopy images of CrSBr, a two-dimensional magnetic material. “The summit was wonderful and the best academic experience I have had as a first-year college student,” says MICRO intern Gabriella La Cour, who is pursuing a major in chemistry and dual degree biomedical engineering at Spelman College and participates in MICRO through MIT. “I got to meet so many students who were all in grades above me … and I learned a little about how to navigate college as an upperclassman.” “I actually have an extremely close friendship with one of the students, and we keep in touch regularly,” adds La Cour. “Professor Chazot gave valuable advice about applications and recommendation letters that will be useful when I apply to REUs [Research Experiences for Undergraduates] and graduate schools.”Looking to the future, MICRO organizers hope to continue to grow the program’s reach.“We would love to see other schools taking on this model,” says Sandland. “There are a lot of opportunities out there. The more departments, research groups, and mentors that get involved with this program, the more impact it can have.” 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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      in Data Management & Statistics

      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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      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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      Startup accelerates progress toward light-speed computing

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      Our ability to cram ever-smaller transistors onto a chip has enabled today’s age of ubiquitous computing. But that approach is finally running into limits, with some experts declaring an end to Moore’s Law and a related principle, known as Dennard’s Scaling.

      Those developments couldn’t be coming at a worse time. Demand for computing power has skyrocketed in recent years thanks in large part to the rise of artificial intelligence, and it shows no signs of slowing down.

      Now Lightmatter, a company founded by three MIT alumni, is continuing the remarkable progress of computing by rethinking the lifeblood of the chip. Instead of relying solely on electricity, the company also uses light for data processing and transport. The company’s first two products, a chip specializing in artificial intelligence operations and an interconnect that facilitates data transfer between chips, use both photons and electrons to drive more efficient operations.

      “The two problems we are solving are ‘How do chips talk?’ and ‘How do you do these [AI] calculations?’” Lightmatter co-founder and CEO Nicholas Harris PhD ’17 says. “With our first two products, Envise and Passage, we’re addressing both of those questions.”

      In a nod to the size of the problem and the demand for AI, Lightmatter raised just north of $300 million in 2023 at a valuation of $1.2 billion. Now the company is demonstrating its technology with some of the largest technology companies in the world in hopes of reducing the massive energy demand of data centers and AI models.

      “We’re going to enable platforms on top of our interconnect technology that are made up of hundreds of thousands of next-generation compute units,” Harris says. “That simply wouldn’t be possible without the technology that we’re building.”

      From idea to $100K

      Prior to MIT, Harris worked at the semiconductor company Micron Technology, where he studied the fundamental devices behind integrated chips. The experience made him see how the traditional approach for improving computer performance — cramming more transistors onto each chip — was hitting its limits.

      “I saw how the roadmap for computing was slowing, and I wanted to figure out how I could continue it,” Harris says. “What approaches can augment computers? Quantum computing and photonics were two of those pathways.”

      Harris came to MIT to work on photonic quantum computing for his PhD under Dirk Englund, an associate professor in the Department of Electrical Engineering and Computer Science. As part of that work, he built silicon-based integrated photonic chips that could send and process information using light instead of electricity.

      The work led to dozens of patents and more than 80 research papers in prestigious journals like Nature. But another technology also caught Harris’s attention at MIT.

      “I remember walking down the hall and seeing students just piling out of these auditorium-sized classrooms, watching relayed live videos of lectures to see professors teach deep learning,” Harris recalls, referring to the artificial intelligence technique. “Everybody on campus knew that deep learning was going to be a huge deal, so I started learning more about it, and we realized that the systems I was building for photonic quantum computing could actually be leveraged to do deep learning.”

      Harris had planned to become a professor after his PhD, but he realized he could attract more funding and innovate more quickly through a startup, so he teamed up with Darius Bunandar PhD ’18, who was also studying in Englund’s lab, and Thomas Graham MBA ’18. The co-founders successfully launched into the startup world by winning the 2017 MIT $100K Entrepreneurship Competition.

      Seeing the light

      Lightmatter’s Envise chip takes the part of computing that electrons do well, like memory, and combines it with what light does well, like performing the massive matrix multiplications of deep-learning models.

      “With photonics, you can perform multiple calculations at the same time because the data is coming in on different colors of light,” Harris explains. “In one color, you could have a photo of a dog. In another color, you could have a photo of a cat. In another color, maybe a tree, and you could have all three of those operations going through the same optical computing unit, this matrix accelerator, at the same time. That drives up operations per area, and it reuses the hardware that’s there, driving up energy efficiency.”

      Passage takes advantage of light’s latency and bandwidth advantages to link processors in a manner similar to how fiber optic cables use light to send data over long distances. It also enables chips as big as entire wafers to act as a single processor. Sending information between chips is central to running the massive server farms that power cloud computing and run AI systems like ChatGPT.

      Both products are designed to bring energy efficiencies to computing, which Harris says are needed to keep up with rising demand without bringing huge increases in power consumption.

      “By 2040, some predict that around 80 percent of all energy usage on the planet will be devoted to data centers and computing, and AI is going to be a huge fraction of that,” Harris says. “When you look at computing deployments for training these large AI models, they’re headed toward using hundreds of megawatts. Their power usage is on the scale of cities.”

      Lightmatter is currently working with chipmakers and cloud service providers for mass deployment. Harris notes that because the company’s equipment runs on silicon, it can be produced by existing semiconductor fabrication facilities without massive changes in process.

      The ambitious plans are designed to open up a new path forward for computing that would have huge implications for the environment and economy.

      “We’re going to continue looking at all of the pieces of computers to figure out where light can accelerate them, make them more energy efficient, and faster, and we’re going to continue to replace those parts,” Harris says. “Right now, we’re focused on interconnect with Passage and on compute with Envise. But over time, we’re going to build out the next generation of computers, and it’s all going to be centered around light.” More

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      Power when the sun doesn’t shine

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      In 2016, at the huge Houston energy conference CERAWeek, MIT materials scientist Yet-Ming Chiang found himself talking to a Tesla executive about a thorny problem: how to store the output of solar panels and wind turbines for long durations.        

      Chiang, the Kyocera Professor of Materials Science and Engineering, and Mateo Jaramillo, a vice president at Tesla, knew that utilities lacked a cost-effective way to store renewable energy to cover peak levels of demand and to bridge the gaps during windless and cloudy days. They also knew that the scarcity of raw materials used in conventional energy storage devices needed to be addressed if renewables were ever going to displace fossil fuels on the grid at scale.

      Energy storage technologies can facilitate access to renewable energy sources, boost the stability and reliability of power grids, and ultimately accelerate grid decarbonization. The global market for these systems — essentially large batteries — is expected to grow tremendously in the coming years. A study by the nonprofit LDES (Long Duration Energy Storage) Council pegs the long-duration energy storage market at between 80 and 140 terawatt-hours by 2040. “That’s a really big number,” Chiang notes. “Every 10 people on the planet will need access to the equivalent of one EV [electric vehicle] battery to support their energy needs.”

      In 2017, one year after they met in Houston, Chiang and Jaramillo joined forces to co-found Form Energy in Somerville, Massachusetts, with MIT graduates Marco Ferrara SM ’06, PhD ’08 and William Woodford PhD ’13, and energy storage veteran Ted Wiley.

      “There is a burgeoning market for electrical energy storage because we want to achieve decarbonization as fast and as cost-effectively as possible,” says Ferrara, Form’s senior vice president in charge of software and analytics.

      Investors agreed. Over the next six years, Form Energy would raise more than $800 million in venture capital.

      Bridging gaps

      The simplest battery consists of an anode, a cathode, and an electrolyte. During discharge, with the help of the electrolyte, electrons flow from the negative anode to the positive cathode. During charge, external voltage reverses the process. The anode becomes the positive terminal, the cathode becomes the negative terminal, and electrons move back to where they started. Materials used for the anode, cathode, and electrolyte determine the battery’s weight, power, and cost “entitlement,” which is the total cost at the component level.

      During the 1980s and 1990s, the use of lithium revolutionized batteries, making them smaller, lighter, and able to hold a charge for longer. The storage devices Form Energy has devised are rechargeable batteries based on iron, which has several advantages over lithium. A big one is cost.

      Chiang once declared to the MIT Club of Northern California, “I love lithium-ion.” Two of the four MIT spinoffs Chiang founded center on innovative lithium-ion batteries. But at hundreds of dollars a kilowatt-hour (kWh) and with a storage capacity typically measured in hours, lithium-ion was ill-suited for the use he now had in mind.

      The approach Chiang envisioned had to be cost-effective enough to boost the attractiveness of renewables. Making solar and wind energy reliable enough for millions of customers meant storing it long enough to fill the gaps created by extreme weather conditions, grid outages, and when there is a lull in the wind or a few days of clouds.

      To be competitive with legacy power plants, Chiang’s method had to come in at around $20 per kilowatt-hour of stored energy — one-tenth the cost of lithium-ion battery storage.

      But how to transition from expensive batteries that store and discharge over a couple of hours to some as-yet-undefined, cheap, longer-duration technology?

      “One big ball of iron”

      That’s where Ferrara comes in. Ferrara has a PhD in nuclear engineering from MIT and a PhD in electrical engineering and computer science from the University of L’Aquila in his native Italy. In 2017, as a research affiliate at the MIT Department of Materials Science and Engineering, he worked with Chiang to model the grid’s need to manage renewables’ intermittency.

      How intermittent depends on where you are. In the United States, for instance, there’s the windy Great Plains; the sun-drenched, relatively low-wind deserts of Arizona, New Mexico, and Nevada; and the often-cloudy Pacific Northwest.

      Ferrara, in collaboration with Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society and her MIT team, modeled four representative locations in the United States and concluded that energy storage with capacity costs below roughly $20/kWh and discharge durations of multiple days would allow a wind-solar mix to provide cost-competitive, firm electricity in resource-abundant locations.

      Now that they had a time frame, they turned their attention to materials. At the price point Form Energy was aiming for, lithium was out of the question. Chiang looked at plentiful and cheap sulfur. But a sulfur, sodium, water, and air battery had technical challenges.

      Thomas Edison once used iron as an electrode, and iron-air batteries were first studied in the 1960s. They were too heavy to make good transportation batteries. But this time, Chiang and team were looking at a battery that sat on the ground, so weight didn’t matter. Their priorities were cost and availability.

      “Iron is produced, mined, and processed on every continent,” Chiang says. “The Earth is one big ball of iron. We wouldn’t ever have to worry about even the most ambitious projections of how much storage that the world might use by mid-century.” If Form ever moves into the residential market, “it’ll be the safest battery you’ve ever parked at your house,” Chiang laughs. “Just iron, air, and water.”

      Scientists call it reversible rusting. While discharging, the battery takes in oxygen and converts iron to rust. Applying an electrical current converts the rusty pellets back to iron, and the battery “breathes out” oxygen as it charges. “In chemical terms, you have iron, and it becomes iron hydroxide,” Chiang says. “That means electrons were extracted. You get those electrons to go through the external circuit, and now you have a battery.”

      Form Energy’s battery modules are approximately the size of a washer-and-dryer unit. They are stacked in 40-foot containers, and several containers are electrically connected with power conversion systems to build storage plants that can cover several acres.

      The right place at the right time

      The modules don’t look or act like anything utilities have contracted for before.

      That’s one of Form’s key challenges. “There is not widespread knowledge of needing these new tools for decarbonized grids,” Ferrara says. “That’s not the way utilities have typically planned. They’re looking at all the tools in the toolkit that exist today, which may not contemplate a multi-day energy storage asset.”

      Form Energy’s customers are largely traditional power companies seeking to expand their portfolios of renewable electricity. Some are in the process of decommissioning coal plants and shifting to renewables.

      Ferrara’s research pinpointing the need for very low-cost multi-day storage provides key data for power suppliers seeking to determine the most cost-effective way to integrate more renewable energy.

      Using the same modeling techniques, Ferrara and team show potential customers how the technology fits in with their existing system, how it competes with other technologies, and how, in some cases, it can operate synergistically with other storage technologies.

      “They may need a portfolio of storage technologies to fully balance renewables on different timescales of intermittency,” he says. But other than the technology developed at Form, “there isn’t much out there, certainly not within the cost entitlement of what we’re bringing to market.”  Thanks to Chiang and Jaramillo’s chance encounter in Houston, Form has a several-year lead on other companies working to address this challenge. 

      In June 2023, Form Energy closed its biggest deal to date for a single project: Georgia Power’s order for a 15-megawatt/1,500-megawatt-hour system. That order brings Form’s total amount of energy storage under contracts with utility customers to 40 megawatts/4 gigawatt-hours. To meet the demand, Form is building a new commercial-scale battery manufacturing facility in West Virginia.

      The fact that Form Energy is creating jobs in an area that lost more than 10,000 steel jobs over the past decade is not lost on Chiang. “And these new jobs are in clean tech. It’s super exciting to me personally to be doing something that benefits communities outside of our traditional technology centers.

      “This is the right time for so many reasons,” Chiang says. He says he and his Form Energy co-founders feel “tremendous urgency to get these batteries out into the world.”

      This article appears in the Winter 2024 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      Putting AI into the hands of people with problems to solve

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      As Media Lab students in 2010, Karthik Dinakar SM ’12, PhD ’17 and Birago Jones SM ’12 teamed up for a class project to build a tool that would help content moderation teams at companies like Twitter (now X) and YouTube. The project generated a huge amount of excitement, and the researchers were invited to give a demonstration at a cyberbullying summit at the White House — they just had to get the thing working.

      The day before the White House event, Dinakar spent hours trying to put together a working demo that could identify concerning posts on Twitter. Around 11 p.m., he called Jones to say he was giving up.

      Then Jones decided to look at the data. It turned out Dinakar’s model was flagging the right types of posts, but the posters were using teenage slang terms and other indirect language that Dinakar didn’t pick up on. The problem wasn’t the model; it was the disconnect between Dinakar and the teens he was trying to help.

      “We realized then, right before we got to the White House, that the people building these models should not be folks who are just machine-learning engineers,” Dinakar says. “They should be people who best understand their data.”

      The insight led the researchers to develop point-and-click tools that allow nonexperts to build machine-learning models. Those tools became the basis for Pienso, which today is helping people build large language models for detecting misinformation, human trafficking, weapons sales, and more, without writing any code.

      “These kinds of applications are important to us because our roots are in cyberbullying and understanding how to use AI for things that really help humanity,” says Jones.

      As for the early version of the system shown at the White House, the founders ended up collaborating with students at nearby schools in Cambridge, Massachusetts, to let them train the models.

      “The models those kids trained were so much better and nuanced than anything I could’ve ever come up with,” Dinakar says. “Birago and I had this big ‘Aha!’ moment where we realized empowering domain experts — which is different from democratizing AI — was the best path forward.”

      A project with purpose

      Jones and Dinakar met as graduate students in the Software Agents research group of the MIT Media Lab. Their work on what became Pienso started in Course 6.864 (Natural Language Processing) and continued until they earned their master’s degrees in 2012.

      It turned out 2010 wasn’t the last time the founders were invited to the White House to demo their project. The work generated a lot of enthusiasm, but the founders worked on Pienso part time until 2016, when Dinakar finished his PhD at MIT and deep learning began to explode in popularity.

      “We’re still connected to many people around campus,” Dinakar says. “The exposure we had at MIT, the melding of human and computer interfaces, widened our understanding. Our philosophy at Pienso couldn’t be possible without the vibrancy of MIT’s campus.”

      The founders also credit MIT’s Industrial Liaison Program (ILP) and Startup Accelerator (STEX) for connecting them to early partners.

      One early partner was SkyUK. The company’s customer success team used Pienso to build models to understand their customer’s most common problems. Today those models are helping to process half a million customer calls a day, and the founders say they have saved the company over £7 million pounds to date by shortening the length of calls into the company’s call center.

      “The difference between democratizing AI and empowering people with AI comes down to who understands the data best — you or a doctor or a journalist or someone who works with customers every day?” Jones says. “Those are the people who should be creating the models. That’s how you get insights out of your data.”

      In 2020, just as Covid-19 outbreaks began in the U.S., government officials contacted the founders to use their tool to better understand the emerging disease. Pienso helped experts in virology and infectious disease set up machine-learning models to mine thousands of research articles about coronaviruses. Dinakar says they later learned the work helped the government identify and strengthen critical supply chains for drugs, including the popular antiviral remdesivir.

      “Those compounds were surfaced by a team that did not know deep learning but was able to use our platform,” Dinakar says.

      Building a better AI future

      Because Pienso can run on internal servers and cloud infrastructure, the founders say it offers an alternative for businesses being forced to donate their data by using services offered by other AI companies.

      “The Pienso interface is a series of web apps stitched together,” Dinakar explains. “You can think of it like an Adobe Photoshop for large language models, but in the web. You can point and import data without writing a line of code. You can refine the data, prepare it for deep learning, analyze it, give it structure if it’s not labeled or annotated, and you can walk away with fine-tuned, large language model in a matter of 25 minutes.”

      Earlier this year, Pienso announced a partnership with GraphCore, which provides a faster, more efficient computing platform for machine learning. The founders say the partnership will further lower barriers to leveraging AI by dramatically reducing latency.

      “If you’re building an interactive AI platform, users aren’t going to have a cup of coffee every time they click a button,” Dinakar says. “It needs to be fast and responsive.”

      The founders believe their solution is enabling a future where more effective AI models are developed for specific use cases by the people who are most familiar with the problems they are trying to solve.

      “No one model can do everything,” Dinakar says. “Everyone’s application is different, their needs are different, their data is different. It’s highly unlikely that one model will do everything for you. It’s about bringing a garden of models together and allowing them to collaborate with each other and orchestrating them in a way that makes sense — and the people doing that orchestration should be the people who understand the data best.” More

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      Generating opportunities with generative AI

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      Talking with retail executives back in 2010, Rama Ramakrishnan came to two realizations. First, although retail systems that offered customers personalized recommendations were getting a great deal of attention, these systems often provided little payoff for retailers. Second, for many of the firms, most customers shopped only once or twice a year, so companies didn’t really know much about them.

      “But by being very diligent about noting down the interactions a customer has with a retailer or an e-commerce site, we can create a very nice and detailed composite picture of what that person does and what they care about,” says Ramakrishnan, professor of the practice at the MIT Sloan School of Management. “Once you have that, then you can apply proven algorithms from machine learning.”

      These realizations led Ramakrishnan to found CQuotient, a startup whose software has now become the foundation for Salesforce’s widely adopted AI e-commerce platform. “On Black Friday alone, CQuotient technology probably sees and interacts with over a billion shoppers on a single day,” he says.

      After a highly successful entrepreneurial career, in 2019 Ramakrishnan returned to MIT Sloan, where he had earned master’s and PhD degrees in operations research in the 1990s. He teaches students “not just how these amazing technologies work, but also how do you take these technologies and actually put them to use pragmatically in the real world,” he says.

      Additionally, Ramakrishnan enjoys participating in MIT executive education. “This is a great opportunity for me to convey the things that I have learned, but also as importantly, to learn what’s on the minds of these senior executives, and to guide them and nudge them in the right direction,” he says.

      For example, executives are understandably concerned about the need for massive amounts of data to train machine learning systems. He can now guide them to a wealth of models that are pre-trained for specific tasks. “The ability to use these pre-trained AI models, and very quickly adapt them to your particular business problem, is an incredible advance,” says Ramakrishnan.

      Rama Ramakrishnan – Utilizing AI in Real World Applications for Intelligent WorkVideo: MIT Industrial Liaison Program

      Understanding AI categories

      “AI is the quest to imbue computers with the ability to do cognitive tasks that typically only humans can do,” he says. Understanding the history of this complex, supercharged landscape aids in exploiting the technologies.

      The traditional approach to AI, which basically solved problems by applying if/then rules learned from humans, proved useful for relatively few tasks. “One reason is that we can do lots of things effortlessly, but if asked to explain how we do them, we can’t actually articulate how we do them,” Ramakrishnan comments. Also, those systems may be baffled by new situations that don’t match up to the rules enshrined in the software.

      Machine learning takes a dramatically different approach, with the software fundamentally learning by example. “You give it lots of examples of inputs and outputs, questions and answers, tasks and responses, and get the computer to automatically learn how to go from the input to the output,” he says. Credit scoring, loan decision-making, disease prediction, and demand forecasting are among the many tasks conquered by machine learning.

      But machine learning only worked well when the input data was structured, for instance in a spreadsheet. “If the input data was unstructured, such as images, video, audio, ECGs, or X-rays, it wasn’t very good at going from that to a predicted output,” Ramakrishnan says. That means humans had to manually structure the unstructured data to train the system.

      Around 2010 deep learning began to overcome that limitation, delivering the ability to directly work with unstructured input data, he says. Based on a longstanding AI strategy known as neural networks, deep learning became practical due to the global flood tide of data, the availability of extraordinarily powerful parallel processing hardware called graphics processing units (originally invented for video games) and advances in algorithms and math.

      Finally, within deep learning, the generative AI software packages appearing last year can create unstructured outputs, such as human-sounding text, images of dogs, and three-dimensional models. Large language models (LLMs) such as OpenAI’s ChatGPT go from text inputs to text outputs, while text-to-image models such as OpenAI’s DALL-E can churn out realistic-appearing images.

      Rama Ramakrishnan – Making Note of Little Data to Improve Customer ServiceVideo: MIT Industrial Liaison Program

      What generative AI can (and can’t) do

      Trained on the unimaginably vast text resources of the internet, a LLM’s “fundamental capability is to predict the next most likely, most plausible word,” Ramakrishnan says. “Then it attaches the word to the original sentence, predicts the next word again, and keeps on doing it.”

      “To the surprise of many, including a lot of researchers, an LLM can do some very complicated things,” he says. “It can compose beautifully coherent poetry, write Seinfeld episodes, and solve some kinds of reasoning problems. It’s really quite remarkable how next-word prediction can lead to these amazing capabilities.”

      “But you have to always keep in mind that what it is doing is not so much finding the correct answer to your question as finding a plausible answer your question,” Ramakrishnan emphasizes. Its content may be factually inaccurate, irrelevant, toxic, biased, or offensive.

      That puts the burden on users to make sure that the output is correct, relevant, and useful for the task at hand. “You have to make sure there is some way for you to check its output for errors and fix them before it goes out,” he says.

      Intense research is underway to find techniques to address these shortcomings, adds Ramakrishnan, who expects many innovative tools to do so.

      Finding the right corporate roles for LLMs

      Given the astonishing progress in LLMs, how should industry think about applying the software to tasks such as generating content?

      First, Ramakrishnan advises, consider costs: “Is it a much less expensive effort to have a draft that you correct, versus you creating the whole thing?” Second, if the LLM makes a mistake that slips by, and the mistaken content is released to the outside world, can you live with the consequences?

      “If you have an application which satisfies both considerations, then it’s good to do a pilot project to see whether these technologies can actually help you with that particular task,” says Ramakrishnan. He stresses the need to treat the pilot as an experiment rather than as a normal IT project.

      Right now, software development is the most mature corporate LLM application. “ChatGPT and other LLMs are text-in, text-out, and a software program is just text-out,” he says. “Programmers can go from English text-in to Python text-out, as well as you can go from English-to-English or English-to-German. There are lots of tools which help you write code using these technologies.”

      Of course, programmers must make sure the result does the job properly. Fortunately, software development already offers infrastructure for testing and verifying code. “This is a beautiful sweet spot,” he says, “where it’s much cheaper to have the technology write code for you, because you can very quickly check and verify it.”

      Another major LLM use is content generation, such as writing marketing copy or e-commerce product descriptions. “Again, it may be much cheaper to fix ChatGPT’s draft than for you to write the whole thing,” Ramakrishnan says. “However, companies must be very careful to make sure there is a human in the loop.”

      LLMs also are spreading quickly as in-house tools to search enterprise documents. Unlike conventional search algorithms, an LLM chatbot can offer a conversational search experience, because it remembers each question you ask. “But again, it will occasionally make things up,” he says. “In terms of chatbots for external customers, these are very early days, because of the risk of saying something wrong to the customer.”

      Overall, Ramakrishnan notes, we’re living in a remarkable time to grapple with AI’s rapidly evolving potentials and pitfalls. “I help companies figure out how to take these very transformative technologies and put them to work, to make products and services much more intelligent, employees much more productive, and processes much more efficient,” he says. More

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      MIT at the 2023 Venice Biennale

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      The Venice Architecture Biennale, the world’s largest and most visited exhibition focusing on architecture, is once again featuring work by many MIT faculty, students, and alumni. On view through Nov. 26, the 2023 biennale, curated by Ghanaian-Scottish architect, academic, and novelist Lesley Lokko, is showcasing projects responding to the theme of “The Laboratory of Change.”

      Architecture and Planning and curator of the previous Venice Biennale. “Our students, faculty, and alumni have responded to the speculative theme with innovative projects at a range of scales and in varied media.”

      Below are descriptions of MIT-related projects and activities.

      MIT faculty participants

      Xavi Laida Aguirre, assistant professor of architecture

      Project: Everlasting Plastics

      Project description: SPACES, a nonprofit alternative art organization based in Cleveland, Ohio, and the U.S. Department of State’s Bureau of Educational and Cultural Affairs are behind the U.S. Pavilion’s exhibition at this year’s biennale. The theme, Everlasting Plastics, provides a platform for artists and designers to engage audiences in reframing the overabundance of plastic detritus in our waterways, landfills, and streets as a rich resource. Aguirre’s installation covers two rooms and holds a series of partial scenographies examining indoor proofing materials such as coatings, rubbers, gaskets, bent aluminum, silicone, foam, cement board, and beveled edges.

      Yolande Daniels, associate professor of architecture

      Project: The BLACK City Astrolabe: A Constellation of African Diasporic Women

      Project description: From the multiple displacements of race and gender, enter “The BLACK City Astrolabe,” a space-time field comprised of a 3D map and a 24-hour cycle of narratives that reorder the forces of subjugation, devaluation, and displacement through the spaces and events of African diasporic women. The diaspora map traces the flows of descendants of Africa (whether voluntary or forced) atop the visible tension between the mathematical regularity of meridians of longitude and the biases of international date lines.

      In this moment we are running out of time. The meridians and timeline decades are indexed to an infinite conical projection metered in decades. It structures both the diaspora map and timeline and serves as a threshold to project future structures and events. “The BLACK City Astrolabe” is a vehicle to proactively contemplate things that have happened, that are happening, and that will happen. Yesterday, a “Black” woman went to the future, and here she is.

      Mark Jarzombek, professor of architecture

      Project: Kishkindha NY

      Project description: “Kishkindha NY (Office of (Un)Certainty Research: Mark Jarzombek and Vikramaditya Parakash)” is inspired by an imagined forest-city as described in the ancient Indian text the Ramayana. It comes into being not through the limitations of human agency, but through a multi-species creature that destroys and rebuilds. It is exhibited as a video (Space, Time, Existence) and as a special dance performance.

      Ana Miljački, professor of architecture

      Team: Ana Miljački, professor of architecture and director of Critical Broadcasting Lab, MIT; Ous Abou Ras, MArch candidate; Julian Geltman, MArch; Recording and Design, faculty of Dramatic Arts, Belgrade; Calvin Zhong, MArch candidate. Sound design and production: Pavle Dinulović, assistant professor, Department of Sound Recording and Design, University of Arts in Belgrade.

      Collaborators: Melika Konjičanin, researcher, faculty of architecture, Sarajevo; Ana Martina Bakić, assistant professor, head of department of drawing and visual design, faculty of architecture, Zagreb; Jelica Jovanović, Grupa Arhitekata, Belgrade; Andrew Lawler, Belgrade; Sandro Đukić, CCN Images, Zagreb; Other Tomorrows, Boston.

      Project: The Pilgrimage/Pionirsko hodočašće

      Project description:  The artifacts that constitute Yugoslavia’s socialist architectural heritage, and especially those instrumental in the ideological wiring of several postwar generations for anti-fascism and inclusive living, have been subject to many forms of local and global political investment in forgetting their meaning, as well as to vandalism. The “Pilgrimage” synthesizes “memories” from Yugoslavian childhood visits to myriad postwar anti-fascist memorial monuments and offers them in a shifting and spatial multi-channel video presentation accompanied by a nonlinear documentary soundscape, presenting thus anti-fascism and unity as political and activist positions available (and necessary) today, for the sake of the future. Supported by: MIT Center for Art, Science, and Technology (CAST) Mellon Faculty Grant.

      Adèle Naudé Santos, professor of architecture, planning, and urban design; and Mohamad Nahleh, lecturer in architecture and urbanism; in collaboration with the Beirut Urban Lab at the American University of Beirut

      MIT research team: Ghida El Bsat, Joude Mabsout, Sarin Gacia Vosgerichian, Lasse Rau

      Project: Housing as Infrastructure

      Project description: On Aug. 4, 2020, an estimated 2,750 tons of ammonium nitrate stored at the Port of Beirut exploded, resulting in the deaths of more than 200 people and the devastation of port-adjacent neighborhoods. With over 200,000 housing units in disrepair, exploitative real estate ventures, and the lack of equitable housing policies, we viewed the port blast as a potential escalation of the mechanisms that have produced the ongoing affordable housing crisis across the city. 

      The Dar Group requested proposals to rethink the affected part of the city, through MIT’s Norman B. Leventhal Center for Advanced Urbanism. To best ground our design proposal, we invited the Beirut Urban Lab at the American University of Beirut to join us. We chose to work on the heavily impacted low-rise and high-density neighborhood of Mar Mikhael. Our resultant urban strategy anchors housing within a corridor of shared open spaces. Housing is inscribed within this network and sustained through an adaptive system defined by energy-efficiency and climate responsiveness. Cross-ventilation sweeps through the project on all sides, with solar panel lined roofs integrated to always provide adequate levels of electricity for habitation. These strategies are coupled with an array of modular units designed to echo the neighborhood’s intimate quality — all accessible through shared ramps and staircases. Within this context, housing itself becomes the infrastructure, guiding circulation, managing slopes, integrating green spaces, and providing solar energy across the community. 

      Rafi Segal, associate professor of architecture and urbanism, director of the Future Urban Collectives Lab, director of the SMArchS program; and Susannah Drake.

      Contributors: Olivia Serra, William Minghao Du

      Project:  From Redlining to Blue Zoning: Equity and Environmental Risk, Miami 2100 (2021)

      Project description: As part of Susannah Drake and Rafi Segal’s ongoing work on “Coastal Urbanism,” this project examines the legacy of racial segregation in South Florida and the existential threat that climate change poses to communities in Miami. Through models of coops and community-owned urban blocks, this project seeks to empower formerly disenfranchised communities with new methods of equity capture, allowing residents whose parents and grandparents suffered from racial discrimination to build wealth and benefit from increased real estate value and development.

      Nomeda Urbonas, Art, Culture, and Technology research affiliate; and Gediminas Urbonas, ACT associate professor

      Project: The Swamp Observatory

      Project description: “The Swamp Observatory” augmented reality app is a result of two-year collaboration with a school in Gotland Island in the Baltic Sea, arguably the most polluted sea in the world. Developed as a conceptual playground and a digital tool to augment reality with imaginaries for new climate commons, the app offers new perspective to the planning process, suggesting eco-monsters as emergent ecology for the planned stormwater ponds in the new sustainable city. 

      Sarah Williams, associate professor, technology and urban planning

      Team members: listed here.

      Project: DISTANCE UNKNOWN: RISKS AND OPPORTUNITIES OF MIGRATION IN THE AMERICAS 

      Project description: On view are visualizations made by the MIT Civic Data Design Lab and the United Nations World Food Program that helped to shape U.S. migration policy. The exhibition is built from a unique dataset collected from 4,998 households surveyed in El Salvador, Guatemala, and Honduras. A tapestry woven out of money and constructed by the hands of Central America migrants illustrates that migrants spent $2.2 billion to migrate from Central America in 2021.

      MIT student curators

      Carmelo Ignaccolo, PhD candidate, Department of Urban Studies and Planning (DUSP)

      Curator: Carmelo Ignaccolo; advisor: Sarah Williams; researchers: Emily Levenson (DUSP), Melody Phu (MIT), Leo Saenger (Harvard University), Yuke Zheng (Harvard); digital animation designer: Ting Zhang

      Exhibition Design Assistant: Dila Ozberkman (architecture and DUSP)

      Project: The Consumed City 

      Project description: “The Consumed City” narrates a spatial investigation of “overtourism” in the historic city of Venice by harnessing granular data on lodging, dining, and shopping. The exhibition presents two large maps and digital animations to showcase the complexity of urban tourism and to reveal the spatial interplay between urban tourism and urban features, such as landmarks, bridges, and street patterns. By leveraging by-product geospatial datasets and advancing visualization techniques, “The Consumed City” acts as a prototype to call for novel policymaking tools in cities “consumed” by “overtourism.”

      MIT-affiliated auxiliary events

      Rania Ghosn, associate professor of architecture and urbanism, El Hadi Jazairy, Anhong Li, and Emma Jurczynski, with initial contributions from Marco Nieto and Zhifei Xu. Graphic design: Office of Luke Bulman.

      Project: Climate Inheritance

      Project description: “Climate Inheritance” is a speculative design research publication that reckons with the complexity of “heritage” and “world” in the Anthropocene Epoch. The impacts of climate change on heritage sites — from Venice flooding to extinction in the Galapagos Islands — have garnered empathetic attention in a media landscape that has otherwise mostly failed to communicate the urgency of the climate crisis. In a strategic subversion of the media aura of heritage, the project casts World Heritage sites as narrative figures to visualize pervasive climate risks all while situating the present emergency within the wreckage of other ends of worlds, replete with the salvages of extractivism, racism, and settler colonialism.   

      Rebuilding Beirut: Using Data to Co-Design a New Future

      SA+P faculty, researchers, and students are participating in the sixth biennial architecture exhibition “Time Space Existence,” presented by the European Cultural Center. The exhibit showcases three collaborative research and design proposals that support the rebuilding efforts in Beirut following the catastrophic explosion at the Port of Beirut in August 2020.

      “Living Heritage Atlas” captures the significance and vulnerability of Beirut’s cultural heritage. 

      “City Scanner” tracks the environmental impacts of the explosion and the subsequent rebuilding efforts. “Community Streets” supports the redesign of streets and public space. 

      The work is supported by the Dar Group Urban Seed Grant Fund at MIT’s Norman B. Leventhal Center for Advanced Urbanism.

      Team members:Living Heritage AtlasCivic Data Design Lab and Future Heritage Lab at MITAssociate Professor Sarah Williams, co-principal investigator (PI)Associate Professor Azra Aksamija, co-PICity Scanner Senseable City Lab at MIT with the American University of Beirut and FAE Technology Professor Carlo Ratti, co-PIFábio Duarte, co-PISimone Mora, research and project leadCommunity Streets City Form Lab at MIT with the American University of BeirutAssociate Professor Andres Sevtsuk, co-PIProfessor Maya Abou-Zeid, co-PISchool of Architecture and Planning alumni participants   Rodrigo Escandón Cesarman SMArchS Design ’20 (co-curator, Mexican Pavilion)Felecia Davis PhD ’17 Design and Computation, SOFTLAB@PSU (Penn State University)Jaekyung Jung SM ’10, (with the team for the Korean pavilion)Vijay Rajkumar MArch ’22 (with the team for the Bahrain Pavilion)

      Other MIT alumni participants

      Basis with GKZ

      Team: Emily Mackevicius PhD ’18, brain and cognitive sciences, with Zenna Tavares, Kibwe Tavares, Gaika Tavares, and Eli Bingham

      Project description: The nonprofit research group works on rethinking AI as a “reasoning machine.” Their two goals are to develop advanced technological models and to make society able to tackle “intractable problems.” Their approach to technology is founded less on pattern elaboration than on the Bayes’ hypothesis, the ability of machines to work on abductive reasoning, which is the same used by the human mind. Two city-making projects model cities after interaction between experts and stakeholders, and representation is at the heart of the dialogue. More