How telecommunications cables can image the ground beneath us

Mishael Quraishi named 2025 Churchill Scholar

MIT senior Mishael Quraishi has been selected as a 2025-26 Churchill Scholar and will undertake an MPhil in archaeological research at Cambridge University in the U.K. this fall.

Quraishi, who is majoring in material sciences and archeology with a minor in ancient and medieval studies, envisions a future career as a materials scientist, using archeological methods to understand how ancient techniques can be applied to modern problems.

At the Masic Lab at MIT, Quraishi was responsible for studying Egyptian blue, the world’s oldest synthetic pigment, to uncover ancient methods for mass production. Through this research, she secured an internship at the Metropolitan Museum of Art’s Department of Scientific Research, where she characterized pigments on the Amathus sarcophagus. Last fall, she presented her findings to kick off the International Roundtable on Polychromy at the Getty Museum. Quraishi has continued research in the Masic lab and her work on the “Blue Room” of Pompeii was featured on NBC nightly news.

Outside of research, Quraishi has been active in MIT’s makerspace and art communities. She has created engravings and acrylic pourings in the MIT MakerWorkshop, metal sculptures in the MIT Forge, and colored glass rods in the MIT Metropolis makerspace. Quraishi also plays the piano and harp and has sung with the Harvard Summer Chorus and the Handel and Haydn Society. She currently serves as the president of the Society for Undergraduates in Materials Science (SUMS) and captain of the lightweight women’s rowing team that won MIT’s first Division I national championship title in 2022.

“We are delighted that Mishael will have the opportunity to expand her important and interesting research at Cambridge University,” says Kim Benard, associate dean of distinguished fellowships. “Her combination of scientific inquiry, humanistic approach, and creative spirit make her an ideal representative of MIT.”

The Churchill Scholarship is a highly competitive fellowship that annually offers 16 American students the opportunity to pursue a funded graduate degree in science, mathematics, or engineering at Churchill College within Cambridge University. The scholarship, which was established in 1963, honors former British Prime Minister Winston Churchill’s vision of U.S.-U.K. scientific exchange. Since 2017, two additional Kanders Churchill Scholarships have been awarded each year for studies in science policy.

MIT students interested in learning more about the Churchill Scholarship should contact Benard in MIT Career Advising and Professional Development.

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Aligning AI with human values

Senior Audrey Lorvo is researching AI safety, which seeks to ensure increasingly intelligent AI models are reliable and can benefit humanity. The growing field focuses on technical challenges like robustness and AI alignment with human values, as well as societal concerns like transparency and accountability. Practitioners are also concerned with the potential existential risks associated with increasingly powerful AI tools.

“Ensuring AI isn’t misused or acts contrary to our intentions is increasingly important as we approach artificial general intelligence (AGI),” says Lorvo, a computer science, economics, and data science major. AGI describes the potential of artificial intelligence to match or surpass human cognitive capabilities.

An MIT Schwarzman College of Computing Social and Ethical Responsibilities of Computing (SERC) scholar, Lorvo looks closely at how AI might automate AI research and development processes and practices. A member of the Big Data research group, she’s investigating the social and economic implications associated with AI’s potential to accelerate research on itself and how to effectively communicate these ideas and potential impacts to general audiences including legislators, strategic advisors, and others.

Lorvo emphasizes the need to critically assess AI’s rapid advancements and their implications, ensuring organizations have proper frameworks and strategies in place to address risks. “We need to both ensure humans reap AI’s benefits and that we don’t lose control of the technology,” she says. “We need to do all we can to develop it safely.”

Her participation in efforts like the AI Safety Technical Fellowship reflect her investment in understanding the technical aspects of AI safety. The fellowship provides opportunities to review existing research on aligning AI development with considerations of potential human impact. “The fellowship helped me understand AI safety’s technical questions and challenges so I can potentially propose better AI governance strategies,” she says. According to Lorvo, companies on AI’s frontier continue to push boundaries, which means we’ll need to implement effective policies that prioritize human safety without impeding research.

Value from human engagement

When arriving at MIT, Lorvo knew she wanted to pursue a course of study that would allow her to work at the intersection of science and the humanities. The variety of offerings at the Institute made her choices difficult, however.

“There are so many ways to help advance the quality of life for individuals and communities,” she says, “and MIT offers so many different paths for investigation.”

Beginning with economics — a discipline she enjoys because of its focus on quantifying impact — Lorvo investigated math, political science, and urban planning before choosing Course 6-14.

“Professor Joshua Angrist’s econometrics classes helped me see the value in focusing on economics, while the data science and computer science elements appealed to me because of the growing reach and potential impact of AI,” she says. “We can use these tools to tackle some of the world’s most pressing problems and hopefully overcome serious challenges.”

Lorvo has also pursued concentrations in urban studies and planning and international development.

As she’s narrowed her focus, Lorvo finds she shares an outlook on humanity with other members of the MIT community like the MIT AI Alignment group, from whom she learned quite a bit about AI safety. “Students care about their marginal impact,” she says.

Marginal impact, the additional effect of a specific investment of time, money, or effort, is a way to measure how much a contribution adds to what is already being done, rather than focusing on the total impact. This can potentially influence where people choose to devote their resources, an idea that appeals to Lorvo.

“In a world of limited resources, a data-driven approach to solving some of our biggest challenges can benefit from a tailored approach that directs people to where they’re likely to do the most good,” she says. “If you want to maximize your social impact, reflecting on your career choice’s marginal impact can be very valuable.”

Lorvo also values MIT’s focus on educating the whole student and has taken advantage of opportunities to investigate disciplines like philosophy through MIT Concourse, a program that facilitates dialogue between science and the humanities. Concourse hopes participants gain guidance, clarity, and purpose for scientific, technical, and human pursuits.

Student experiences at the Institute

Lorvo invests her time outside the classroom in creating memorable experiences and fostering relationships with her classmates. “I’m fortunate that there’s space to balance my coursework, research, and club commitments with other activities, like weightlifting and off-campus initiatives,” she says. “There are always so many clubs and events available across the Institute.”

These opportunities to expand her worldview have challenged her beliefs and exposed her to new interest areas that have altered her life and career choices for the better. Lorvo, who is fluent in French, English, Spanish, and Portuguese, also applauds MIT for the international experiences it provides for students.

“I’ve interned in Santiago de Chile and Paris with MISTI and helped test a water vapor condensing chamber that we designed in a fall 2023 D-Lab class in collaboration with the Madagascar Polytechnic School and Tatirano NGO [nongovernmental organization],” she says, “and have enjoyed the opportunities to learn about addressing economic inequality through my International Development and D-Lab classes.”

As president of MIT’s Undergraduate Economics Association, Lorvo connects with other students interested in economics while continuing to expand her understanding of the field. She enjoys the relationships she’s building while also participating in the association’s events throughout the year. “Even as a senior, I’ve found new campus communities to explore and appreciate,” she says. “I encourage other students to continue exploring groups and classes that spark their interests throughout their time at MIT.”

After graduation, Lorvo wants to continue investigating AI safety and researching governance strategies that can help ensure AI’s safe and effective deployment.

“Good governance is essential to AI’s successful development and ensuring humanity can benefit from its transformative potential,” she says. “We must continue to monitor AI’s growth and capabilities as the technology continues to evolve.”

Understanding technology’s potential impacts on humanity, doing good, continually improving, and creating spaces where big ideas can see the light of day continue to drive Lorvo. Merging the humanities with the sciences animates much of what she does. “I always hoped to contribute to improving people’s lives, and AI represents humanity’s greatest challenge and opportunity yet,” she says. “I believe the AI safety field can benefit from people with interdisciplinary experiences like the kind I’ve been fortunate to gain, and I encourage anyone passionate about shaping the future to explore it.”

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Eleven MIT faculty receive Presidential Early Career Awards

Eleven MIT faculty, including nine from the School of Engineering and two from the School of Science, were awarded the Presidential Early Career Award for Scientists and Engineers (PECASE). More than 15 additional MIT alumni were also honored. 

Established in 1996 by President Bill Clinton, the PECASE is awarded to scientists and engineers “who show exceptional potential for leadership early in their research careers.” The latest recipients were announced by the White House on Jan. 14 under President Joe Biden. Fourteen government agencies recommended researchers for the award.

The MIT faculty and alumni honorees are among 400 scientists and engineers recognized for innovation and scientific contributions. Those from the School of Engineering and School of Science who were honored are:

• Tamara Broderick, associate professor in the Department of Electrical Engineering and Computer Science (EECS), was nominated by the Office of Naval Research for her project advancing “Lightweight representations for decentralized learning in data-rich environments.”
   
• Michael James Carbin SM ’09, PhD ’15, associate professor in the Department of EECS, was nominated by the National Science Foundation (NSF) for his CAREER award, a project that developed techniques to execute programs reliably on approximate and unreliable computation substrates.
   
• Christina Delimitrou, the KDD Career Development Professor in Communications and Technology and associate Professor in the Department of EECS, was nominated by the NSF for her group’s work on redesigning the cloud system stack given new cloud programming frameworks like microservices and serverless compute, as well as designing hardware acceleration techniques that make cloud data centers more predictable and resource-efficient.
   
• Netta Engelhardt, the Biedenharn Career Development Associate Professor of Physics, was nominated by the Department of Energy for her research on the black hole information paradox and its implications for the fundamental quantum structure of space and time.
   
• Robert Gilliard Jr., the Novartis Associate Professor of Chemistry, was selected based the results generated from his 2020 National Science Foundation CAREER award entitled: "CAREER: Boracycles with Unusual Bonding as Creative Strategies for Main-Group Functional Materials.”
   
• Heather Janine Kulik PD ’09, PhD ’09, the Lammot du Pont Professor of Chemical Engineering, was nominated by the NSF for her 2019 proposal entitled “CAREER: Revealing spin-state-dependent reactivity in open-shell single atom catalysts with systematically-improvable computational tools.”
   
• Nuno Loureiro, professor in the Department of Nuclear Science and Engineering, was nominated by the NSF for his work on the generation and amplification of magnetic fields in the universe.
   
• Robert Macfarlane, associate professor in the Department of Materials Science and Engineering, was nominated by the Department of Defense (DoD)’s Air Force Office of Scientific Research. His research focuses on making new materials using molecular and nanoscale building blocks.
   
• Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering in the Department of Mechanical Engineering, was nominated by the DoD for her ARO-funded research that explored leveraging biological actuators in next-generation robots that can sense and adapt to their environments.
   
• Ellen Roche, the Latham Family Career Development Professor and associate department head in the Department of Mechanical Engineering, was nominated by the NSF for her CAREER award, a project that aims to create a cutting-edge benchtop model combining soft robotics and organic tissue to accurately simulate the motions of the heart and diaphragm.
   
• Justin Wilkerson, a visiting associate professor in the Department of Aeronautics and Astronautics, was nominated by the Air Force Office of Scientific Research (AFOSR) for his research primarily related to the design and optimization of novel multifunctional composite materials that can survive extreme environments.

Additional MIT alumni who were honored include: Elaheh Ahmadi ’20, MNG ’21; Ambika Bajpayee MNG ’07, PhD ’15; Katherine Bouman SM ’13, PhD ’17; Walter Cheng-Wan Lee ’95, MNG ’95, PhD ’05; Ismaila Dabo PhD ’08; Ying Diao SM ’10, PhD ’12; Eno Ebong ’99; Soheil Feizi- Khankandi SM ’10, PhD ’16; Mark Finlayson SM ’01, PhD ’12; Chelsea B. Finn ’14; Grace Xiang Gu SM ’14, PhD ’18; David Michael Isaacson PhD ’06, AF ’16; Lewei Lin ’05; Michelle Sander PhD ’12; Kevin Solomon SM ’08, PhD ’12; and Zhiting Tian PhD ’14.

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Introducing the MIT Generative AI Impact Consortium

From crafting complex code to revolutionizing the hiring process, generative artificial intelligence is reshaping industries faster than ever before — pushing the boundaries of creativity, productivity, and collaboration across countless domains.

Enter the MIT Generative AI Impact Consortium, a collaboration between industry leaders and MIT’s top minds. As MIT President Sally Kornbluth highlighted last year, the Institute is poised to address the societal impacts of generative AI through bold collaborations. Building on this momentum and established through MIT’s Generative AI Week and impact papers, the consortium aims to harness AI’s transformative power for societal good, tackling challenges before they shape the future in unintended ways.

“Generative AI and large language models [LLMs] are reshaping everything, with applications stretching across diverse sectors,” says Anantha Chandrakasan, dean of the School of Engineering and MIT’s chief innovation and strategy officer, who leads the consortium. “As we push forward with newer and more efficient models, MIT is committed to guiding their development and impact on the world.”

Chandrakasan adds that the consortium’s vision is rooted in MIT’s core mission. “I am thrilled and honored to help advance one of President Kornbluth’s strategic priorities around artificial intelligence,” he says. “This initiative is uniquely MIT — it thrives on breaking down barriers, bringing together disciplines, and partnering with industry to create real, lasting impact. The collaborations ahead are something we’re truly excited about.”

Developing the blueprint for generative AI’s next leap

The consortium is guided by three pivotal questions, framed by Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and co-chair of the GenAI Dean’s oversight group, that go beyond AI’s technical capabilities and into its potential to transform industries and lives:

1. How can AI-human collaboration create outcomes that neither could achieve alone?
2. What is the dynamic between AI systems and human behavior, and how do we maximize the benefits while steering clear of risks?
3. How can interdisciplinary research guide the development of better, safer AI technologies that improve human life?

Generative AI continues to advance at lightning speed, but its future depends on building a solid foundation. “Everybody recognizes that large language models will transform entire industries, but there's no strong foundation yet around design principles,” says Tim Kraska, associate professor of electrical engineering and computer science in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-faculty director of the consortium.

“Now is a perfect time to look at the fundamentals — the building blocks that will make generative AI more effective and safer to use,” adds Kraska.

"What excites me is that this consortium isn’t just academic research for the distant future — we’re working on problems where our timelines align with industry needs, driving meaningful progress in real time," says Vivek F. Farias, the Patrick J. McGovern (1959) Professor at the MIT Sloan School of Management, and co-faculty director of the consortium.

A “perfect match” of academia and industry

At the heart of the Generative AI Impact Consortium are six founding members: Analog Devices, The Coca-Cola Co., OpenAI, Tata Group, SK Telecom, and TWG Global. Together, they will work hand-in-hand with MIT researchers to accelerate breakthroughs and address industry-shaping problems.

The consortium taps into MIT’s expertise, working across schools and disciplines — led by MIT’s Office of Innovation and Strategy, in collaboration with the MIT Schwarzman College of Computing and all five of MIT’s schools.

“This initiative is the ideal bridge between academia and industry,” says Chandrakasan. “With companies spanning diverse sectors, the consortium brings together real-world challenges, data, and expertise. MIT researchers will dive into these problems to develop cutting-edge models and applications into these different domains.”

Industry partners: Collaborating on AI’s evolution

At the core of the consortium’s mission is collaboration — bringing MIT researchers and industry partners together to unlock generative AI’s potential while ensuring its benefits are felt across society.

Among the founding members is OpenAI, the creator of the generative AI chatbot ChatGPT.

“This type of collaboration between academics, practitioners, and labs is key to ensuring that generative AI evolves in ways that meaningfully benefit society,” says Anna Makanju, vice president of global impact at OpenAI, adding that OpenAI “is eager to work alongside MIT’s Generative AI Consortium to bridge the gap between cutting-edge AI research and the real-world expertise of diverse industries.”

The Coca-Cola Co. recognizes an opportunity to leverage AI innovation on a global scale. “We see a tremendous opportunity to innovate at the speed of AI and, leveraging The Coca-Cola Company's global footprint, make these cutting-edge solutions accessible to everyone,” says Pratik Thakar, global vice president and head of generative AI. “Both MIT and The Coca-Cola Company are deeply committed to innovation, while also placing equal emphasis on the legally and ethically responsible development and use of technology.”

For TWG Global, the consortium offers the ideal environment to share knowledge and drive advancements. “The strength of the consortium is its unique combination of industry leaders and academia, which fosters the exchange of valuable lessons, technological advancements, and access to pioneering research,” says Drew Cukor, head of data and artificial intelligence transformation. Cukor adds that TWG Global “is keen to share its insights and actively engage with leading executives and academics to gain a broader perspective of how others are configuring and adopting AI, which is why we believe in the work of the consortium.”

The Tata Group views the collaboration as a platform to address some of AI’s most pressing challenges. “The consortium enables Tata to collaborate, share knowledge, and collectively shape the future of generative AI, particularly in addressing urgent challenges such as ethical considerations, data privacy, and algorithmic biases,” says Aparna Ganesh, vice president of Tata Sons Ltd.

Similarly, SK Telecom sees its involvement as a launchpad for growth and innovation. Suk-geun (SG) Chung, SK Telecom executive vice president and chief AI global officer, explains, “Joining the consortium presents a significant opportunity for SK Telecom to enhance its AI competitiveness in core business areas, including AI agents, AI semiconductors, data centers (AIDC), and physical AI,” says Chung. “By collaborating with MIT and leveraging the SK AI R&D Center as a technology control tower, we aim to forecast next-generation generative AI technology trends, propose innovative business models, and drive commercialization through academic-industrial collaboration.”

Alan Lee, chief technology officer of Analog Devices (ADI), highlights how the consortium bridges key knowledge gaps for both his company and the industry at large. “ADI can’t hire a world-leading expert in every single corner case, but the consortium will enable us to access top MIT researchers and get them involved in addressing problems we care about, as we also work together with others in the industry towards common goals,” he says.

The consortium will host interactive workshops and discussions to identify and prioritize challenges. “It’s going to be a two-way conversation, with the faculty coming together with industry partners, but also industry partners talking with each other,” says Georgia Perakis, the John C Head III Dean (Interim) of the MIT Sloan School of Management and professor of operations management, operations research and statistics, who serves alongside Huttenlocher as co-chair of the GenAI Dean’s oversight group.

Preparing for the AI-enabled workforce of the future

With AI poised to disrupt industries and create new opportunities, one of the consortium’s core goals is to guide that change in a way that benefits both businesses and society.

“When the first commercial digital computers were introduced [the UNIVAC was delivered to the U.S. Census Bureau in 1951], people were worried about losing their jobs,” says Kraska. “And yes, jobs like large-scale, manual data entry clerks and human ‘computers,’ people tasked with doing manual calculations, largely disappeared over time. But the people impacted by those first computers were trained to do other jobs.”

The consortium aims to play a key role in preparing the workforce of tomorrow by educating global business leaders and employees on generative AI evolving uses and applications. With the pace of innovation accelerating, leaders face a flood of information and uncertainty.

“When it comes to educating leaders about generative AI, it’s about helping them navigate the complexity of the space right now, because there’s so much hype and hundreds of papers published daily,” says Kraska. “The hard part is understanding which developments could actually have a chance of changing the field and which are just tiny improvements. There's a kind of FOMO [fear of missing out] for leaders that we can help reduce.”

Defining success: Shared goals for generative AI impact

Success within the initiative is defined by shared progress, open innovation, and mutual growth. “Consortium participants recognize, I think, that when I share my ideas with you, and you share your ideas with me, we’re both fundamentally better off,” explains Farias. “Progress on generative AI is not zero-sum, so it makes sense for this to be an open-source initiative.”

While participants may approach success from different angles, they share a common goal of advancing generative AI for broad societal benefit. “There will be many success metrics,” says Perakis. “We’ll educate students, who will be networking with companies. Companies will come together and learn from each other. Business leaders will come to MIT and have discussions that will help all of us, not just the leaders themselves.”

For Analog Devices’ Alan Lee, success is measured in tangible improvements that drive efficiency and product innovation: “For us at ADI, it’s a better, faster quality of experience for our customers, and that could mean better products. It could mean faster design cycles, faster verification cycles, and faster tuning of equipment that we already have or that we’re going to develop for the future. But beyond that, we want to help the world be a better, more efficient place.”

Ganesh highlights success through the lens of real-world application. “Success will also be defined by accelerating AI adoption within Tata companies, generating actionable knowledge that can be applied in real-world scenarios, and delivering significant advantages to our customers and stakeholders,” she says.

Generative AI is no longer confined to isolated research labs — it’s driving innovation across industries and disciplines. At MIT, the technology has become a campus-wide priority, connecting researchers, students, and industry leaders to solve complex challenges and uncover new opportunities. “It's truly an MIT initiative,” says Farias, “one that’s much larger than any individual or department on campus.”

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David Darmofal SM ’91, PhD ’93 named vice chancellor for undergraduate and graduate education

David L. Darmofal SM ’91, PhD ’93 will serve as MIT’s next vice chancellor for undergraduate and graduate education, effective Feb. 17. Chancellor Melissa Nobles announced Darmofal’s appointment today in a letter to the MIT community.

Darmofal succeeds Ian A. Waitz, who stepped down in May to become MIT’s vice president for research, and Daniel E. Hastings, who has been serving in an interim capacity.

A creative innovator in research-based teaching and learning, Darmofal is the Jerome C. Hunsaker Professor of Aeronautics and Astronautics. Since 2017, he and his wife Claudia have served as heads of house at The Warehouse, an MIT graduate residence.

“Dave knows the ins and outs of education and student life at MIT in a way that few do,” Nobles says. “He’s a head of house, an alum, and the parent of a graduate. Dave will bring decades of first-hand experience to the role.”

“An MIT education is incredibly special, combining passionate students, staff, and faculty striving to use knowledge and discovery to drive positive change for the world,” says Darmofal. “I am grateful for this opportunity to play a part in supporting MIT’s academic mission.”

Darmofal’s leadership experience includes service from 2008 to 2011 as associate and interim department head in the Department of Aeronautics and Astronautics, overseeing undergraduate and graduate programs. He was the AeroAstro director of digital education from 2020 to 2022, including leading the department’s response to remote learning during the Covid-19 pandemic. He currently serves as director of the MIT Aerospace Computational Science and Engineering Laboratory and is a member of the Center for Computational Science and Engineering (CCSE) in the MIT Stephen A. Schwarzman College of Computing.

As an MIT faculty member and administrator, Darmofal has been involved in designing more flexible degree programs, developing open digital-learning opportunities, creating first-year advising seminars, and enhancing professional and personal development opportunities for students. He also contributed his expertise in engineering pedagogy to the development of the Schwarzman College of Computing’s Common Ground efforts, to address the need for computing education across many disciplines.

“MIT students, staff, and faculty share a common bond as problem solvers. Talk to any of us about an MIT education, and you will get an earful on not only what we need to do better, but also how we can actually do it. The Office of the Vice Chancellor can help bring our community of problem solvers together to enable improvements in our academics,” says Darmofal.

Overseeing the academic arm of the Chancellor’s Office, the vice chancellor’s portfolio is extensive. Darmofal will lead professionals across more than a dozen units, covering areas such as recruitment and admissions, financial aid, student systems, advising, professional and career development, pedagogy, experiential learning, and support for MIT’s more than 100 graduate programs. He will also work collaboratively with many of MIT’s student organizations and groups, including with the leaders of the Undergraduate Association and the Graduate Student Council, and administer the relationship with the graduate student union.

“Dave will be a critical part of my office’s efforts to strengthen and expand critical connections across all areas of student life and learning,” Nobles says. She credits the search advisory group, co-chaired by professors Laurie Boyer and Will Tisdale, in setting the right tenor for such an important role and leading a thorough, inclusive process.

Darmofal’s research is focused on computational methods for partial differential equations, especially fluid dynamics. He earned his SM and PhD degrees in aeronautics and astronautics in 1991 and 1993, respectively, from MIT, and his BS in aerospace engineering in 1989 from the University of Michigan. Prior to joining MIT in 1998, he was an assistant professor in the Department of Aerospace Engineering at Texas A&M University from 1995 to 1998. Currently, he is the chair of AeroAstro’s Undergraduate Committee and the graduate officer for the CCSE PhD program.

“I want to echo something that Dan Hastings said recently,” Darmofal says. “We have a lot to be proud of when it comes to an MIT education. It’s more accessible than it has ever been. It’s innovative, with unmatched learning opportunities here and around the world. It’s home to academic research labs that attract the most talented scholars, creators, experimenters, and engineers. And ultimately, it prepares graduates who do good.”

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User-friendly system can help developers build more efficient simulations and AI models

The neural network artificial intelligence models used in applications like medical image processing and speech recognition perform operations on hugely complex data structures that require an enormous amount of computation to process. This is one reason deep-learning models consume so much energy.

To improve the efficiency of AI models, MIT researchers created an automated system that enables developers of deep learning algorithms to simultaneously take advantage of two types of data redundancy. This reduces the amount of computation, bandwidth, and memory storage needed for machine learning operations.

Existing techniques for optimizing algorithms can be cumbersome and typically only allow developers to capitalize on either sparsity or symmetry — two different types of redundancy that exist in deep learning data structures.

By enabling a developer to build an algorithm from scratch that takes advantage of both redundancies at once, the MIT researchers’ approach boosted the speed of computations by nearly 30 times in some experiments.

Because the system utilizes a user-friendly programming language, it could optimize machine-learning algorithms for a wide range of applications. The system could also help scientists who are not experts in deep learning but want to improve the efficiency of AI algorithms they use to process data. In addition, the system could have applications in scientific computing.

“For a long time, capturing these data redundancies has required a lot of implementation effort. Instead, a scientist can tell our system what they would like to compute in a more abstract way, without telling the system exactly how to compute it,” says Willow Ahrens, an MIT postdoc and co-author of a paper on the system, which will be presented at the International Symposium on Code Generation and Optimization.

She is joined on the paper by lead author Radha Patel ’23, SM ’24 and senior author Saman Amarasinghe, a professor in the Department of Electrical Engineering and Computer Science (EECS) and a principal researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Cutting out computation

In machine learning, data are often represented and manipulated as multidimensional arrays known as tensors. A tensor is like a matrix, which is a rectangular array of values arranged on two axes, rows and columns. But unlike a two-dimensional matrix, a tensor can have many dimensions, or axes, making tensors more difficult to manipulate.

Deep-learning models perform operations on tensors using repeated matrix multiplication and addition — this process is how neural networks learn complex patterns in data. The sheer volume of calculations that must be performed on these multidimensional data structures requires an enormous amount of computation and energy.

But because of the way data in tensors are arranged, engineers can often boost the speed of a neural network by cutting out redundant computations.

For instance, if a tensor represents user review data from an e-commerce site, since not every user reviewed every product, most values in that tensor are likely zero. This type of data redundancy is called sparsity. A model can save time and computation by only storing and operating on non-zero values.

In addition, sometimes a tensor is symmetric, which means the top half and bottom half of the data structure are equal. In this case, the model only needs to operate on one half, reducing the amount of computation. This type of data redundancy is called symmetry.

“But when you try to capture both of these optimizations, the situation becomes quite complex,” Ahrens says.

To simplify the process, she and her collaborators built a new compiler, which is a computer program that translates complex code into a simpler language that can be processed by a machine. Their compiler, called SySTeC, can optimize computations by automatically taking advantage of both sparsity and symmetry in tensors.

They began the process of building SySTeC by identifying three key optimizations they can perform using symmetry.

First, if the algorithm’s output tensor is symmetric, then it only needs to compute one half of it. Second, if the input tensor is symmetric, then algorithm only needs to read one half of it. Finally, if intermediate results of tensor operations are symmetric, the algorithm can skip redundant computations.

Simultaneous optimizations

To use SySTeC, a developer inputs their program and the system automatically optimizes their code for all three types of symmetry. Then the second phase of SySTeC performs additional transformations to only store non-zero data values, optimizing the program for sparsity.

In the end, SySTeC generates ready-to-use code.

“In this way, we get the benefits of both optimizations. And the interesting thing about symmetry is, as your tensor has more dimensions, you can get even more savings on computation,” Ahrens says.

The researchers demonstrated speedups of nearly a factor of 30 with code generated automatically by SySTeC.

Because the system is automated, it could be especially useful in situations where a scientist wants to process data using an algorithm they are writing from scratch.

In the future, the researchers want to integrate SySTeC into existing sparse tensor compiler systems to create a seamless interface for users. In addition, they would like to use it to optimize code for more complicated programs.

This work is funded, in part, by Intel, the National Science Foundation, the Defense Advanced Research Projects Agency, and the Department of Energy.

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