MIT’s Department of Electrical Engineering and Computer Science (EECS) has appointed six new faculty members. One has already joined the department, while five others will arrive in 2018 and early 2019.
Song Han will join EECS as an assistant professor in July 2018. His research focuses on energy-efficient deep learning at the intersection of machine learning and computer architecture. He proposed the Deep Compression algorithm, which can compress neural networks by 17 to 49 times while fully preserving prediction accuracy. He also designed the first hardware accelerator that can perform inference directly on a compressed sparse model, which results in significant speedup and energy saving. His work has been featured by O’Reilly, TechEmergence, and The Next Platform, among others. He led research efforts in model compression and hardware acceleration and won best-paper awards at the International Conference on Learning Representations and the International Symposium on Field-Programmable Gate Arrays. Han received a PhD and a master’s degree from Stanford University, both in electrical engineering.
Phillip Isola will join EECS as an assistant professor in July 2018. Currently a fellow at OpenAI, he studies visual intelligence from the perspective of both minds and machines. Isola received both a National Science Foundation (NSF) Graduate Fellowship and a NSF Postdoctoral Fellowship. Isola received a PhD in brain and cognitive studies from MIT and a bachelor’s degree in computer science from Yale University.
Tim Kraska will join EECS as an associate professor in January 2018. Currently an assistant professor in the Department of Computer Science at Brown University, Kraska focuses on building systems for interactive data exploration, machine learning, and transactional systems for modern hardware, especially the next generation of networks. Kraska received a PhD from ETH Zurich, then spent three years as a postdoc in the AMPLab at the University of California at Berkeley, where he worked on hybrid human-machine database systems and cloud-scale data management systems. Kraska was recently selected as a 2017 Alfred P. Sloan Research Fellow in computer science. He has also received a National Science Foundation CAREER Award, an Air Force Young Investigator award, two Very Large Data Bases conference best-demo awards, and a best-paper award from the IEEE International Conference on Data Engineering.
Farnaz Niroui will join EECS as an assistant professor in January 2019. She is currently a Miller Postdoctoral Fellow at the University of California at Berkeley. She received PhD and master’s degrees in electrical engineering from MIT and a bachelor’s degree in nanotechnology engineering from the University of Waterloo in Nanotechnology Engineering. During her graduate studies, Farnaz was a recipient of the Engineering Research Council of Canada Scholarship, and was selected to the Rising Stars for EECS program in 2015 at MIT and in 2016 at Carnegie Mellon University. Her research integrates electrical engineering with materials science and chemistry to develop hybrid nanofabrication techniques to enable precise yet scalable processing of nanoscale architectures capable of uniquely controlling light-matter interactions, electronic transport, and exciton dynamics to engineer new paradigms of active nanoscale devices.
Arvind Satyanarayan will join EECS as an assistant professor in July 2018. He focuses on developing new declarative languages for interactive visualization and leveraging them in new systems for visualization design and data analysis. He is currently a postdoc at Google Brain, working on improving the interpretability of deep learning models through visualization. His research has been recognized with a Google PhD Fellowship and best-paper awards at the IEEE InfoVis and the Association for Computing Machinery Computer-Human Interaction conference. His work has also been deployed on Wikipedia to enable interactive visualizations within articles. Satyanarayan received a PhD in computer science from Stanford University, working with Jeffrey Heer and the University of Washington Interactive Data Lab. He also received a master’s degree from Stanford and a bachelor’s degree from the University of California at San Diego, both in computer science.
Julian Shun joined EECS as an assistant professor in September 2017. His research focuses on both the theory and the practice of parallel algorithms and programming. He is particularly interested in designing algorithms and frameworks for large-scale graph analytics. He is also interested in parallel algorithms for text analytics, concurrent data structures, and methods for deterministic parallelism. Shun has received the ACM Doctoral Dissertation Award, the Carnegie Mellon University (CMU) School of Computer Science Doctoral Dissertation Award, a Facebook Graduate Fellowship, and a best-student-paper award at the Data Compression Conference. Before coming to MIT, he was a postdoctoral Miller Research Fellow at the University of California at Berkeley. Shun received a PhD degree in computer science from CMU and a bachelor’s degree in computer science from UC Berkeley.