Department of EECS announces 2022 Promotions


The Department of Electrical Engineering and Computer Science (EECS) is proud to announce the following promotions:

Adam Belay is being promoted to Associate Professor Without Tenure, effective July 1, 2022. Belay earned his BS and MEng at MIT in 2008 and 2011, respectively, and his PhD in Computer Science at Stanford in 2016. He spent a year as a Software Engineer at Google before joining MIT in July 2017.  Belay has also worked on storage virtualization at VMware Inc. and contributed substantial code to the Linux Kernel for power management right after graduating from MIT in 2008. Belay was appointed the Jamieson Career Development Professor in Electrical Engineering and Computer Science in January 2020. Belay’s research focuses on operating systems and networking, specifically developing practical and efficient methods for microsecond-scale distributed computing, which has many applications pertaining to resource management in data centers. His operating system, Caladan, reallocates server resources on a microsecond scale, resulting in high CPU utilization with low tail latency. Additionally, Belay has contributed to load balancing, and Application-Integrated Far Memory (AIFM) in OS designs. Belay’s microsecond-scale resource management is already being incorporated in commercial OS.

Within MIT EECS, Belay has served on graduate admissions committee and he chairs the SYS research area for graduate admissions. He has developed 6.S060, a new OS class targeted at undergraduates, while turning 6.828 into a graduate-level OS seminar-style class. Belay has served on the program committees of Operating Systems Design and Implementation (OSDI) and Symposium on Operating Systems Principles (SOSP), the top systems conferences, and has received the OSDI Jay Lepreau best paper award.

Mohsen Ghaffari will join the Department of Electrical Engineering and Computer Science as an Associate Professor Without Tenure in April 2022. Ghaffari received his BSc from the Sharif University of Technology in 2010, and his MSc and PhD in EECS from MIT in 2013 and 2016, respectively. He joins MIT from ETH Zurich, where he has been on the faculty since 2016.  Ghaffari’s research has focused on distributed computing and massively parallel algorithms for large graphs. One of his groundbreaking results is that randomness is not required for efficient distributed algorithms. This has solved several major decades-old open problems in the field, e.g., establishing that the maximal independent set problem has an O(log n) round deterministic  distributed algorithm. His works have had impact on other models: depth-width tradeoff lower bounds for GNNs, sparsification techniques for distributed algorithms that also improve the best known massively parallel algorithms and sublinear time approximation algorithms, and breakthroughs in descriptive combinatorics over infinite measurable graphs.

Ghaffari’s doctoral dissertation received the Association for Computing Machinery (ACM) Doctoral Dissertation Honorable Mention Award, the ACM-EATCS Principles of Distributed Computing Doctoral Dissertation Award, and the George M. Sprowls Award for Best Computer Science PhD thesis at MIT. He has received several best paper awards at DIStributed Computing  (DISC) and the Symposium on Discrete Algorithms (SODA), and while at ETH, he received a prestigious European Research Council Starting Grant and the Google Faculty Research Award.

Manya Ghobadi is being promoted to Associate Professor Without Tenure, effective July 1, 2022. She earned her bachelor’s degree in computer engineering from the Sharif University of Technology in 2005, followed by her M.Sc. in Computer Science from the University of Victoria in 2007, and her PhD in Computer Science from the University of Toronto in 2013. She then worked at Google as a software engineer and at Microsoft as a researcher, before joining EECS as an Assistant Professor in October 2018. Ghobadi was appointed the TIBCO Founder Professor in July 2019. Ghobadi’s primary research area is reconfigurable optical fiber networks and their applications in support of machine learning workloads. She is considered the leading expert in networks with reconfigurable physical-layer. Many of the technologies Ghobadi has helped develop are part of real-world systems at Microsoft and Google.

Ghobadi has served as a programming committee member of the premier conferences in computer networks and optics research, including the Association for Computing Machinery’s Special Interest Group on Data Communications (SIGCOMM), Networked Systems Design and Implementation (NSDI), Optical Fiber Communication Conference (OSA OFC), Symposium on Operating Systems Principles (SOSP), Operating Systems Design and Implementation (OSDI), Usenix Annual Technical Conference (ATC), HotNets, and Optica (OSA) Photonics Networks. Additionally, she co-founded the SIGCOMM workshop on Optical Systems Design (OptSys), which has become a re-occurring workshop with over 100 attendees. She has co-chaired several symposiums on the Role of Machine Learning in Optical Systems and was the general chair of ACM Symposium on SDN Research (SOSR) (2018). She has also served as the mentoring co-chair for the N2Women workshop at ACM SIGCOMM in 2017. Ghobadi has won the Google research Excellent Paper Award (twice), and the ACM Internet Measurement Conference Best Paper award. Within EECS, Ghobadi has taught several courses including 6.829, which she has helped to update and modernize. She is currently the chair of the Graduate Admission Committee for the Computer Network (CNET) area.

Song Han is being promoted to Associate Professor Without Tenure, effective July 1, 2022. He received his Ph.D. in Electrical Engineering in 2017 from Stanford University, after which he spent a year at Google Brain as a research scientist. Song joined EECS as an Assistant Professor in July of 2018. Song’s research efforts focus on developing tools and principled methods to enable the application of deep learning on edge devices. Additionally, he has worked on the efficient synthesis of small models for the large diversity of edge devices without incurring customization overhead. His research has resulted in the development of proxylessNAS (a method of searching possible neural architectures to find one that meets performance goals, reducing the search time from >48,000 GPU-hrs to 200 GPU-hrs while meeting accuracy and latency constraints) and, subsequently, the development of Once-For-All (which facilitates the training of a single large-scale deep learning model so the end user can search to find sub-models that meet various constraints). Once-For-All has won first place in several low-power computer vision challenges, and several of Song’s algorithms have been incorporated into commercial systems from NVIDIA, Facebook, Baidu, and Amazon. Among other awards, Song has received Best Paper at ICLR’16 and FPGA’17 and was named to the “35 Innovators Under 35” list by MIT Technology Review for his contribution on “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.” Additionally, Song has received the National Science Founndation (NSF) CAREER Award for “efficient algorithms and hardware for accelerated machine learning”; the Institute of Electrical and Electronics Engineers (IEEE) “AIs 10 to Watch: The Future of AI” award; and Research Awards from Facebook, Google, and Amazon.

Besides teaching core undergraduate classes, including 6.004 and 6.036, Song is both an undergraduate adviser and co-chair of a graduate admission area, and has served on the Microsystems Technology Laboratories (MTL) Grand Challenge Committee. Outside MIT, he has served on committees for multiple conferences, including the International Conference on Learning Representations (ICLR) and the Conference and Workshop on Neural Information Processing Systems (NeurIPS), the International Conference on Computer-Aided Design (ICCAD), IEEE’s High-Performance Computer Architecture (HPCA), the Conference on Machine Learning and Systems (MLSys), and the tinyML Research Symposium.

Phillip Isola is being promoted to Associate Professor Without Tenure, effective July 1, 2022. Phillip Isola joined EECS as an Assistant Professor in July of 2018. He received his Ph.D. in 2015 from the Brain and Cognitive Sciences (BCS) Department at MIT before taking on a postdoctoral position at Berkeley, followed by a visiting research scientist position at Open AI. His research explores learning representations that capture the commonalities between disparate domains, and thereby achieve generality; directly linking experiences via visual translation; and designing representations that can adapt fast. A leader in the use of machine learning to analyze and create images, Isola’s series of 2017 papers introduced a solution to the problem of image translation. Isola’s recent work addresses another fundamental computer vision problem: the requirement of large amounts of labelled, or supervised, training data, which limits most learning-based approaches to computer vision. For this work, Isola was awarded the IEEE Pattern Analysis and Machine Intelligence (PAMI) Young Researcher 2021 Award which was given during CVPR 2021, as well as the Packard Fellowship.

Isola has co-designed a new course, 6.882, “Embodied Intelligence”; re-designed lectures for 6.819/6.869, “Advances in computer vision”; taught recitations of “Machine learning”, 6.036; and organized a deep-learning class pilot. Additionally, he has served as a mentor for Rising Stars, as well as participating in the MIT EECS Academic Job Search Seminar and the mock faculty interviews program. Outside MIT, in addition to being area chair for several conferences, Isola has participated in several activities oriented towards the mentoring of young graduate students, including the Conference on Computer Vision and Pattern Recognition (CVPR) 2019 and International Conference on Computer Vision (ICCV) 2019 Doctoral Consortiums and the Mentor program at the International Conference on Learning Representations (ICLR) 2021.

Arvind Satyanarayan is being promoted to Associate Professor Without Tenure, effective July 1, 2022. Satyanarayan earned his MS and PhD in Computer Science at Stanford in 2014 and 2017, respectively, and his B.S. in Computer Science from UCSD in 2011. After spending a year as a postdoctoral research scientist on the Google Brain team, Satyanarayan joined the Department of EECS in July 2018. Within MIT Computer Science & Artificial Intelligence Laboratory (CSAIL), Satyanarayan leads the Visualization Group, which focuses on visualization to study intelligence augmentation, specifically tools for interactive visualization, sociotechnical impacts of visualization, and machine learning interpretability. His PhD work on Reactive Vega and Vega-Lite has been widely adopted in data science (e.g., via the Altair Python package), in industry (e.g., at Apple, Google, and The LA Times), and in academic research.

Within the Department of EECS, Satyanarayan is known for teaching both 6.170 and a new course which he developed on interactive data visualization, 6.859. He has repeatedly served in the program committees of several major conferences in his area, including ACM Conference on Human Factors in Computing Systems (CHI), the IEEE Visualization Conference (Vis), and served as diversity and inclusion chair for IEEE VIS and in the IEEE Ad Hoc committee on Diversity and Inclusion. His excellence in teaching was recognized by the department with the MIT EECS Kolokotrones Education Award. Additionally, Satyanarayan has received an NSF CAREER award and has been recognized as a National Academy of Science Kavli Fellow.

Adam Chlipala is being promoted to Full Professor, effective July 1, 2022. Chlipala earned his BS from Carnegie Mellon University (CMU) in 2003, and his MS and PhD from Berkeley in 2004 and 2007, respectively. He spent time at Jane Street as a software developer and Harvard as a postdoc before joining MIT in 2011. He was promoted to Associate Professor (AWOT) in 2015 and to Associate Professor with Tenure (AWIT) in 2018. Chlipala is the head of the Programming Languages and Verification Group in CSAIL, where his research focuses on developing methods for integrating the work of software design and verification. He has also done extensive foundational work in building general computational infrastructure to support programming, verification, and automatic code generation. He has applied his techniques to several key systems areas such as file system verification, hardware design, and cryptographic libraries for use in building secure systems. His recent work on cryptographic libraries has been adopted by Google for its Chrome web browser, and his formal semantics for the RISC-V processor have recently been adopted as the official specification for the processor’s instruction set architecture. Among his many honors, Chlipala has been awarded a 2013 NSF CAREER award, a Best Paper award at SOSP 2015 for his FSCQ work on file system verification, the Most Influential Paper award at the International Conference on Functional Programming (ICFP) 2018 and two Communication of the ACM (CACM) research highlights. He was also elected as ACM Distinguished Member in 2019.

Chlipala has developed the new 6.822 class (Formal Reasoning About Algorithms), played an instrumental role in creating 6.009 (Fundamentals of Programming), and wrote the widely used book Certified Programming for Dependent Types (MIT Press, 2013). He won the Ruth and Joel Spira Award for Excellence in Teaching 2019. Additionally, he has helped to run the Graduate Admissions Committee, including developing a web application (using UrWeb) for managing Visit Day, and has served as the Chair of the EECS Computer Science Curriculum Advisory Committee, where he was tasked to make recommendations regarding the programming and theory subject requirements for CS majors.

David Sontag is being promoted to Full Professor, effective July 1, 2022 He earned his PhD in 2010 from MIT before heading to Microsoft for a postdoctoral fellowship, and then New York University, where he was an Assistant Professor of Computer Science and Data Science. Part of Institute for Medical Engineering and Science (IMES), CSAIL, and the J-Clinic for Machine Learning in Health, Sontag joined MIT as Assistant Professor in EECS and IMES in January 2017 and was promoted to Associate Professor with Tenure in 2018. Sontag’s research focuses on advancing machine learning and artificial intelligence, and using these to transform healthcare—his long-term goal is to create artificial intelligence that can take the data available in electronic medical records, reason about a patient’s health, and ultimately help make proactive (instead of reactive) healthcare decisions. To achieve this goal, his research focuses on four challenges: the lack of labeled data to use for supervision when learning; messy data, consisting of multivariate time-series with complex long-range dependencies and substantial missing parts; the statistical limitations of causal inferences from health data; and the challenge of building appropriate trust in AI decision support. His work on antibiotic treatment, and his work on Deep Markov models and sequence modeling, are both considered particularly influential benchmarks within applied ML for healthcare. Sontag has acted as reviewer, area chair and workshop organizer at all major ML conferences, including NeurIPS, the International Conference on Machine Learning (ICML), Conference on Uncertainty in Artificial Intelligence (UAI), and AI-STATS, as well as conferences in ML for healthcare, including Machine Learning for Healthcare (MLHC), and American Medical Informatics Association (AMIA).

Among his many other contributions to MIT, Sontag has served in the EECS AI+D, Broad Institute, and IMES faculty search committees; participated in the EECS committee to develop a proposal for Industry Ph.D. collaboration; and helped to organize the J-Clinic & IMES Machine Learning for Health discussion series. He serves regularly as co-chair of the EECS AI+D Graduate Admissions Committee, and as co-chair of the ML, AI, and Data Science Common Ground Committee. Additionally, he organized the first MIT-wide machine learning graduate student retreat, and the IMES faculty lunch seminar. In addition to his on-campus teaching, Sontag, together with Peter Szolovits, developed the new edX/MITx online course on Machine Learning for Healthcare, an effort for which they received the 2017 Burgess (52) & Elizabeth Jamieson Award for Excellence in Teaching.

Virginia Vassilevska Williams is being promoted to Full Professor, effective July 1, 2022.  Williams earned her BS at CalTech in 2003, and her MS and PhD at CMU in 2007 and 2008, respectively. She did postdocs at Berkeley and Stanford, and was promoted to Assistant Professor at Stanford in 2013. Williams joined MIT in January 2017 as an Associate Professor Without Tenure, and was granted tenure in 2019. Williams’s research focuses on algorithm design and analysis of fundamental problems involving matrices, graphs, and strings. She seeks to determine the precise (asymptotic) time complexity of problems in P (the class of problems solvable in polynomial time). She has designed the fastest algorithm for matrix multiply, and is widely regarded as the leading expert on Fine-Grained Complexity and Fine-Grained Reductions. Williams is also interested in computational social choice issues, such as making elections computationally resistant to manipulation.

Within the theoretical computer science research community, Williams is highly regarded, with six Special Issue papers spanning the years 2008-2019 in the top Theoretical Computer Science conferences: the Symposium on Theory of Computing (STOC), Foundations of Computer Science (FOCS), and the Symposium on Discrete Algorithms (SODA). She was one of very few theoretical computer scientists invited to address the International Congress of Mathematicians in 2018, and has given keynote and plenary talks at the Symposium on Computational Geometry (SOCG) 2021, the International Colloquium on Automata, Languages and Programming (ICALP) 2020, Lunteren Conference on the Mathematics of Operations Research 2020, and the International Symposium on Symbolic & Algebraic Computation (ISAAC) 2019, as well as the inaugural Simons Institute “Breakthroughs” lecture in June 2021. She sits on the Editorial Board for five journals, has co-organized the Women in Theoretical Computer Science (TCS Women) Workshop at every STOC since 2018, co-organized a semester-long program on fine-grained complexity and algorithms at the Simons Institute, and has co-organized workshops and tutorials on various aspects of algorithms and complexity. Additionally, she is a member of the Scientific Advisory Board at the Berkeley Simons Institute, chair of the International Symposium on Parameterized and Exact Computation (IPEC) Nerode Prize Award Committee, and a SafeTOC advocate. Among her many awards, she has received an NSF CAREER award; a Sloan Foundation faculty fellowship; a Google Faculty Research Award; and a Hoover Fellowship from Stanford.

At MIT EECS, she has served as technical co-chair for Rising Stars; taught four different courses and developed new courses on Graph and Matrix Algorithms and on Fine-Grained Complexity; organized the weekly Theory of Computation (TOC) colloquium; served on the PhD admissions committee every year and the EECS faculty search committee since 2019; and has served on the Council on Diversity, Equity, and Inclusion (DEI).