Department of EECS Announces 2024 Promotions

By Jane Halpern

February 28, 2024 | Department of Electrical Engineering and Computer Science

Top row, left to right: Vivienne Sze, Pulkit Agrawal, Mengjia Yan, Kaiming He Bottom row, left to right: Farnaz Niroui, YuFeng (Kevin) Chen, Marzyeh Ghassemi, Dirk Englund, Connor Wilson Coley.

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

Pulkit Agrawal is being promoted to Associate Professor Without Tenure, effective July 1, 2024. Agrawal earned his undergraduate degree from IIT Kanpur and his M.S. and PhD in computer science from the University of California at Berkeley. He joined the Department of EECS as an assistant professor in July 2019. Agrawal is a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and an affiliate member of the Laboratory for Information and Decision Systems (LIDS); additionally, he is the co-founder of SafelyYou, Inc. Agrawal’s research interests span robotics, deep learning, computer vision and reinforcement learning; he explains, “[my] overarching research interest is to build machines that have similar manipulation and locomotion abilities as humans.” Agrawal is a recipient of multiple best paper awards, the Multidisciplinary University Research Initiative (MURI) award, the Sony Faculty Research Award, the Salesforce Research Award, the Amazon Machine Learning Research Award, the Signatures Fellow Award, the Fulbright Science and Technology Award, the Goldman Sachs Global Leadership Award, the OPJEMS and the Sridhar Memorial Prize, among others. 

Within the Department, Agrawal has developed the classes 6.8200 “Sensorimotor Learning” and 6.S897 “Advanced Sensorimotor Learning” and has served on the EECS-BCS joint faculty search committee; as the chair for reinforcement learning (RL) area in EECS Ph.D. admissions; as organizer for the Computational Sensorimotor Learning seminar; and co-organizer for the MIT Robotics Seminar series.

YuFeng (Kevin) Chen is being promoted to Associate Professor Without Tenure, effective July 1, 2024. Chen earned his bachelor’s degree from Cornell and his PhD from Harvard University; after postdoctoral work at Harvard, he joined the Department of EECS as an assistant professor in 2020. Chen is a principal investigator in the Research Laboratory of Electronics (RLE), where his work focuses on developing multifunctional and multimodal insect-scale robots. He developed the first soft-driven micro-aerial-robots powered by dielectric elastomer actuators, and further demonstrated flights resembling insect agility and resilience. He is a recipient of the NSF CAREER award, the Steven Vogel Young Investigator Award, and several best paper awards at top robotics journals and conferences such as TRO 2021, RAL 2020, and IROS 2015.

Within the Department, Chen has contributed greatly to multiple fundamental undergraduate electrical engineering courses, including 6.2000 “Electrical Circuits: Modeling and Design of Physical Systems”, 6.3100 “Dynamical System Modeling and Control Design”, and 6.2210 “Electromagnetic Fields, Forces and Motion”. He has helped with graduate admissions, and served on the MTL Doctoral Dissertation Seminar Series Committee. His gift for teaching and mentorship has been honored with the 2023 Ruth and Joel Spira Award for Excellence in Teaching.

Connor Coley is being promoted to Associate Professor Without Tenure in the Department of Chemical Engineering and the Department of Electrical Engineering and Computer Science, effective July 1, 2024. Coley received his B.S. and Ph.D. in Chemical Engineering from Caltech and MIT, respectively, and did his postdoctoral training at the Broad Institute. His research group at MIT develops computational strategies for small molecule drug discovery, molecular optimization, and synthesis planning. He explains, “A long-term goal of our work is to enable autonomous molecular discovery, where hypotheses are proposed algorithmically and tested via experiments with minimal human intervention.” Key research areas in the group include the design of new neural models for representation learning on molecules, data-driven synthesis planning, in silico strategies for predicting the outcomes of organic reactions, model-guided Bayesian optimization, de novo molecular generation, and structure elucidation. 

Among other honors, Coley has received the AI2050 Early Career Fellowship; is a recipient of C&EN’s “Talented Twelve” award; was named to Forbes Magazine’s “30 Under 30” for Healthcare; and has received the NSF CAREER award and the Bayer Early Excellence in Science Award. Additionally, Coley has distinguished himself as a thoughtful curriculum developer, creating 3.C01[J] “Machine Learning for Molecular Engineering” alongside Rafael Gomez-Bombarelli (Materials Science and Engineering) and Ernest Fraenkel (Biological Engineering), a course for which all three were recognized with the Schwarzman College of Computing’s 2023 Common Ground Award for Excellence in Teaching.

Marzyeh Ghassemi is being promoted to Associate Professor Without Tenure, effective July 1, 2024. Ghassemi earned two bachelor’s degrees from New Mexico State University as a Goldwater Scholar; her MS from Oxford University as a Marshall Scholar; and her PhD from MIT. She joined MIT from the University of Toronto, joining the Department of EECS and the Institute for Medical Engineering & Science (IMES) – the home of the Harvard-MIT Program in Health Sciences and Technology – as an assistant professor in July 2021. She is also affiliated with the Jameel Clinic and CSAIL, and is a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She also founded the non-profit Association for Health Learning and Inference. 

Ghassemi’s research in “Healthy” Machine Learning in the Healthy ML Group creates a rigorous quantitative framework in which to place ML models in a way that is robust and fair in health settings. Her contributions range from socially-aware model construction, to improving subgroup- and shift-robust learning methods, to identifying important insights in model deployment scenarios that have implications in policy, health practice and equity. Among other awards, Ghassemi has been named one of MIT Tech Review’s 35 Innovators Under 35; and has been awarded the 2018 Seth J. Teller Award, the 2023 MIT Prize for Open Data, and a 2024 NSF CAREER Award. Within the department, Ghassemi has revised HST 953 to include more modern machine learning issues; created EECS 6.882 “Ethical Machine Learning in Human Deployments”; and developed a reputation as an excellent mentor while serving on several committees, including the School of Engineering’s Faculty Gender Equity Committee, the IMES Graduate Admissions Committee, and the institutional Presidential Committee for Distinguished Fellowships. 

Kaiming He is appointed to Associate Professor Without Tenure, effective Feb 27, 2024. He earned his bachelor’s degree from Tsinghua University, and his PhD from the Chinese University of Hong Kong before joining Microsoft Research Asia (MSRA) as a Researcher and then Facebook AI Research (FAIR) as a Research Scientist. His research areas include deep learning and computer vision. He is best-known for his work on Deep Residual Networks (ResNets), which have made significant impact on computer vision and broader artificial intelligence; on visual object detection and segmentation, including Faster R-CNN and Mask R-CNN; and on visual self-supervised learning. He explains, “My research currently focuses on building computer models that can learn representations and develop intelligence from and for the complex world. The long-term goal of my research is to augment human intelligence with more capable artificial intelligence.”

He has had multiple prominent positions within the research community, including acting as program chair of ICCV 2023, and as editor of the International Journal of Computer Vision. His awards include the PAMI Young Researcher Award in 2018; three best paper awards, at CVPR 2009, CVPR 2016, and ICCV 2017; two best paper honorable mentions (at ECCV 2018 and CVPR 2021); and an Everingham Prize for selfless contributions to computer vision. 

Farnaz Niroui is being promoted to Associate Professor Without Tenure, effective July 1, 2024. Niroui earned her bachelor’s degree at the University of Waterloo, and her PhD from MIT before taking a postdoctoral position at University of California Berkeley. She returned to MIT as an Assistant Professor in EECS in November 2018, and is a principal investigator in the Research Laboratory of Electronics (RLE). Niroui’s research focuses on pushing the limits of nanoscale engineering towards the atomic scale, where by developing new fabrication and materials integration platforms, she enables new active nanoscale devices and systems for emerging applications in electronics and optoelectronics. For her research contributions, Niroui has been the recipient of awards including the DARPA Young Faculty Award and the NSF CAREER Award. 

Within the Department, she has co-chaired the EECS Rising Stars program, served as a member of the EECS DEI committee, and has been the coordinator for the new EE Nano track curriculum while serving as an often-invited speaker or panelist at events ranging from the MIT Path of Professorship, Graduate Women in Course 6 Research Summit, and MIT Women’s Technology Program, among others. Niroui has taught multiple core classes and co-developed 6.2540 “Nanotechnology: From Atoms to Systems”, a new class with an interactive curriculum where lectures are closely integrated with design-oriented labs and projects to teach the fundamentals of applied quantum mechanics in relation to the design and fabrication of diverse nanotechnologies. For her efforts in creating this unique and groundbreaking class, Niroui has received the EECS Outstanding Educator Award (2022). Beyond teaching in class, Niroui has also supported nanotechnology research and education across MIT by organizing the MIT.nano seminar series and co-chairing the Dresselhaus lectures.  

Mengjia Yan is being promoted to Associate Professor Without Tenure, effective July 1, 2024. Yan earned her bachelor’s degree from Zhejiang University, and her PhD from the University of Illinois at Urbana-Champaign before joining MIT as an Assistant Professor in November 2019. She is a principal investigator within the Computer Science and Artificial Intelligence Laboratory (CSAIL), where her research focuses on computer architecture and security, with a specific focus on hardware security in processor design, side channel attacks and defenses. Her group works on exploiting new micro-architectural vulnerabilities and designing comprehensive and efficient defense mechanisms.

Among other honors, Yan has received the NSF CAREER Award, Intel Rising Star Faculty Award, ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award Honorable Mention, multiple MICRO TopPicks in Computer Architecture and a MICRO best paper award. Within the Department, Yan has designed a new class on secure hardware design, 6.5950, “Secure Hardware Design”, with in-depth lab assignments. The course’s material has already been adapted by other universities, including UC San Diego, CMU, and University of Toronto. Additionally, Yan has taught graduate and undergraduate architecture subjects,  6.823 and 6.004, respectively; co-chaired the EECS Rising Stars Workshop in 2021; served on the Grad Admissions committee, chairing the architecture section in 2020-2023; and served on the 2020 Sprowls + ACM Award Committees.

Dirk Englund is being promoted to Full Professor, effective July 1, 2024. Englund received his BS in Physics from Caltech; following a Fulbright year at TU Eindhoven, he earned his MS and PhD from Stanford University. He was a postdoctoral fellow at Harvard University until 2010, when he started his group as Assistant Professor of Electrical Engineering and of Applied Physics at Columbia University. In 2013, he joined MIT’s Department of Electrical Engineering and Computer Science; he was promoted to Associate Professor without Tenure in 2016, and promoted to Associate Professor with Tenure in 2018. Englund is a principal investigator within the Microsystems Technology Laboratories (MTL) and the Research Laboratory of Electronics (RLE), where his research focuses broadly on the field of photonics applied to quantum information science and engineering and to machine learning; quantum computing; quantum networking & sensing; and photonic integrated circuits for machine learning. His awards include the NSF CAREER award, Sloan fellowship, DARPA YFA, the PECASE, the Optical Society of America Adolph Lomb Medal (the top award for a young researcher in optics), Fellow of Optica (formerly the Optical Society of America), fellow of IEEE, a Web of Science “Highly Cited Researcher” since 2021, awardee of a Alexander von Humboldt Foundation Professorship, and he has co-founded and serves as scientific advisor to several technology companies.

Within the Department, Englund has co-chaired one of the graduate admissions subcommittees in the department, co-organized the Masterworks event, and served on the Lincoln Laboratory-Campus Interaction Committee, among other service contributions. He has taught a number of service and specialty classes (including 6.UAT Oral Communication 6.602 Fundamental of Photonics), and, most notably, led the development of what is one of the first undergraduate quantum engineering lab classes in the nation–a class for which all the abstractions, equipment, and pedagogy needed to be developed from scratch.

Vivienne Sze is being promoted to Full Professor, effective July 1, 2024. Sze earned her bachelor’s degree from the University of Toronto and her master’s degree and PhD from MIT. She was a member of the technical staff in the R&D Center at Texas Instruments (TI), where she designed low-power algorithms and architectures for video coding, before returning to MIT in 2013 as an assistant professor of electrical engineering and computer science. She was promoted to associate professor without tenure in July 2017, and is a principal investigator within the Research Laboratory of Electronics (RLE), Microsystems Technology Laboratories (MTL), and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Sze’s research involves the co-design of energy-aware signal processing algorithms and low-power circuits, architectures, and systems for a broad set of applications, including machine learning, computer vision, robotics, image processing, and video coding. She is currently working on projects focusing on autonomous navigation and embedded artificial intelligence (AI) for health-monitoring applications. Her honors include MIT’s Edgerton Faculty Achievement Award, the Young Investigator Research Program Award from the Air Force Office of Scientific Research, the Young Faculty Award from the Defense Advanced Research Projects Agency,  the Symposium on VLSI Circuits Best Student Paper Award, the CICC Outstanding Invited Paper Award, the IEEE Micro Top Picks Award, and several faculty awards from Google, Facebook, and Qualcomm. As a member of the Joint Collaborative Team on Video Coding, she received the Primetime Engineering Emmy Award for the development of the High-Efficiency Video Coding video compression standard.

Sze, alongside Joel Emer, has codeveloped a considerable body of instructional material surrounding hardware accelerators for deep neural networks, including tutorials at ISCA, MICRO, ISSCC and NeurIPS, an MIT Professional Education short course, a tutorial paper in Proceedings of the IEEE, and finally a residential class on hardware accelerators for neural networks (6.5930, “Hardware Architecture for Deep Learning”) that now counts as part of the graduate qualifying exam. In addition, Sze, Emer, Y.-H. Chen and T.-J. Yang have co-authored a textbook on the subject, entitled “Efficient Processing of Deep Neural Networks”.