Department of EECS Announces Promotions

By Jane Halpern

May 22, 2025 | Department of Electrical Engineering and Computer Science

Top row, left to right: Connor W. Coley, Mohsen Ghaffari, Song Han Bottom row, left to right: Kaiming He, Phillip Isola, Jonathan Ragan-Kelley, Arvind Satyanarayan. All photos courtesy of their subjects.

The Department of EECS is proud to announce the following promotions to Associate Professor with tenure, all effective July 1, 2025: 

Connor Coley is being promoted to Associate Professor with tenure in the Department of Chemical Engineering and the Department of Electrical Engineering and Computer Science. 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, chemical synthesis, and structure elucidation. 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 computational metabolomics.

Among other honors, Coley has received the AI2050 Early Career Fellowship; was recognized as a Samsung AI Researcher of the Year; is a recipient of C&EN’s “Talented Twelve” award; was named to Forbes Magazine’s “30 Under 30” for Healthcare and MIT Technology Review’s Innovators Under 35; has received the NSF CAREER award and the Bayer Early Excellence in Science Award; and was most recently named a Camille Dreyfus Teacher-Scholar. Additionally, Coley has distinguished himself as a committed undergraduate mentor, receiving the 2024 Outstanding UROP Mentor Award, and 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.

Mohsen Ghaffari is being promoted to Associate Professor with tenure. 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, before joining the faculty of ETH Zurich. He joined MIT EECS in July of 2022. His research explores the theory of distributed and parallel computation, and he has had influential work on a range of algorithmic problems, including generic derandomization methods for distributed computing and parallel computing (which resolved several decades-old open problems), improved distributed algorithms for graph problems, sublinear algorithms derived via distributed techniques, and algorithmic and impossibility results for massively parallel computation.

Ghaffari’s work has received several best paper awards, including from the IEEE Symposium on Foundations of Computer Science (FOCS) 2024, the ACM Symposium on Parallel Algorithms and Architectures (SPAA) 2023, the ACM Symposium on Discrete Algorithms (SODA) 2016, and the International Symposium on DIStributed Computing (DISC) 2017 and 2013. While at ETH, he received a prestigious European Research Council Starting Grant and the Google Faculty Research Award. In 2025, he was named a Sloan Research Fellow. During his time at ETH, he developed graduate courses in Advanced Algorithms, Distributed Algorithms, and Massively Parallel Algorithms, plus modules on parallel and distributed graph algorithms for undergraduate courses. At MIT, Ghaffari has done a major revision of the graduate-level course 6.5250, Distributed Algorithms, and also lectures in the undergraduate Introduction to Algorithms class.

Song Han is being promoted to Associate Professor with tenure. He earned his PhD from Stanford, pioneering efficient AI computing techniques such as “Deep Compression” (pruning, quantization) and the “Efficient Inference Engine,” which first introduced weight sparsity to modern AI chips, making it one of the top-5 most cited papers in the 50-year history of ISCA (1953-2023). His innovations, including TinyML and hardware-aware neural architecture search (Once-for-All Network), have advanced AI model deployment on resource-constrained devices. His recent work on LLM quantization/acceleration (SmoothQuant, AWQ, StreamingLLM) has improved efficiency in LLM inference, and was adopted by NVIDIA TensorRT-LLM. 

Han has received best paper awards at ICLR ’16, FPGA ’17, and MLSys ’24, the NSF CAREER Award in 2020, MIT Technology Review’s “35 Innovators Under 35,” IEEE “AI’s 10 to Watch,” and the 2023 Sloan Research Fellowship. He developed the open lecture series EfficientML.ai to share advances in efficient ML research. Within the department, Han has developed a class on tiny machine learning, 6.5940 TinyML and Efficient Deep Learning Computing; has taught and developed original content for 6.191 Computation Structures (the department’s foundational computer architecture course); and has chaired the graduate admissions subcommittee for “machine learning for systems and systems for machine learning”. 

Kaiming He is being promoted to Associate Professor with tenure. He earned his BS from Tsinghua University in 2007 and his PhD from the Chinese University of Hong Kong in 2011 before joining Microsoft Research Asia (MSRA) as a Researcher and then Facebook AI Research (FAIR) as a Research Scientist. He joined the Department of EECS as an associate professor in February 2024, and is affiliated with the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). 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’s 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, alongside the team behind Detectron, an Everingham Prize for selfless contributions to computer vision. He has taught 6.8300 Advances in Computer Vision, as well as a specialized seminar on Deep Generative Models, and is currently serving as a member of the faculty search committee in AI+D. 

Phillip Isola is being promoted to Associate Professor with tenure. Isola 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. He joined MIT in July 2018. Isola’s 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. His most 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. Among other awards, Isola has won the Packard Fellowship Award (2021), the IEEE PAMI Young Researcher Award (2021), the Sloan Fellowship, Samsung AI Researcher of the Year, and the CoRL 2023 Best Paper Award. 

Within the Department, Isola is a member of the 6-4 curriculum committee; has co-designed a new course 6.882 Embodied Intelligence; has updated and redesigned lectures for 6.819 Advances in Computer Vision; and has taught 6.036 Machine Learning. Alongside Stefanie Jegelka, he designed a deep-learning class pilot which has now become a graduate-level course. Additionally, Isola co-authored the textbook Foundations of Computer Vision alongside Antonio Torralba and William Freeman; and has served on multiple faculty search, steering, and admissions committees.

Jonathan Ragan-Kelley is being promoted to Associate Professor with tenure. He obtained his PhD from MIT in 2014, and after spending time as a postdoctoral researcher at Stanford (2014-2016), a visiting scientist at Google (2016-2017), and an Assistant Professor at Berkeley (2017-2019), Ragan-Kelley joined MIT in January of 2020. A pioneer in the development of high-performance domain-specific languages (especially for computer graphics), Ragan-Kelley has repeatedly identified important domains that require significant expert effort to deliver the necessary performance before developing new high-level programming languages that capture this expertise and can deliver high performance with less effort and lower risk of bugs. His computer graphics language Halide has become the industry standard for image processing, and is used in both Google phones and Adobe Photoshop, while his exocompiler, Exo, is used by developers at Apple and Intel, and powers core features on the iPhone. His earlier work, on the system Lightspeed, was used to produce movies for several years at Industrial Light and Magic and was a finalist for a technical Oscar award. 

Ragan-Kelley’s work has earned him (in 2021) the ACM SIGGRAPH Significant New Researcher Award, which is the highest award given by the community to young researchers. His work has also been featured in CACM research highlights in 2018 and 2019. His original Halide publication from 2013, received the PLDI Test of Time Award in 2023; in the same year, he was named a Sloan Research Fellow. While at Berkeley, he developed a graduate class on compilers; while at MIT, Ragan-Kelley has taught 6.172 Software Performance Engineering multiple times; lectured in the fundamentals of programming class; and has headed the CSAIL Visual Computing Community of Research. In 2023, his contributions were acknowledged by the Department with the EECS Outstanding Educator Award.

Arvind Satyanarayan is being promoted to Associate Professor with tenure. Satyanarayan earned his MS and PhD in Computer Science at Stanford in 2014 and 2017, respectively, before 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), he 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 Vega-Altair Python package), in industry (e.g., at Apple, Google, and The LA Times), and in academic research. Among other awards, he has received an NSF CAREER award; a Sloan Research Fellowship; a National Academy of Science Kavli Fellowship; the IEEE VGTC Visualization Significant New Researcher Award, and paper awards at ACM CHI, IUI, IEEE VIS, EuroVis, and ACL.

Within the Department of EECS, Satyanarayan has developed a new course on interactive data visualization & society (6.C35/C85) as part of the College of Computing’s Common Ground subjects. 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 ACM Symposium on User Interface Software and Technology (UIST), and 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 has been recognized by the department with the 2020 Kolokotrones Education Award and the 2021 Seth J. Teller Award for Excellence, Inclusion, and Diversity.