Mohammad Alizadeh is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He completed his graduate studies at Stanford University, earning his Ph.D. in electrical engineering in 2013, and then spent two years at Insieme Networks (a data center networking startup) and Cisco before joining MIT. Alizadeh's research interests are in the areas of computer networks and systems. His current research focuses on learning-based network systems, programmable switching architectures, and network protocols and algorithms for blockchains. Alizadeh's research has garnered significant industry interest. His work on data center transport protocols has been implemented in Linux and Windows and has been adopted by leading network operators such as Microsoft and Google; his work on adaptive network load balancing algorithms has been implemented in Cisco’s flagship data center switching products. Alizadeh is the recipient of several awards, including the NSF CAREER Award (2018), SIGCOMM Rising Star Award (2017), Alfred P. Sloan Research Fellowship (2017), and multiple best paper awards.
Michael Cafarella is Principal Research Scientist in the Computer Science and Artificial Intelligence Laboratory’s Data Systems Group. Cafarella’s research interests include databases, information extraction, data integration, and data mining. Previously, he was an Associate Professor of Computer Science and Engineering at the University of Michigan. He has published extensively in venues such as SIGMOD, VLDB and elsewhere. He received his Ph.D. from the University of Washington, Seattle, in 2009 with advisors Oren Etzioni and Dan Suciu. His academic awards include the NSF CAREER Award, the Sloan Research Fellowship, and the 2018 VLDB Ten-Year Best Paper Award. In addition to his academic work, he co-started (with Doug Cutting) the Hadoop open-source project, which is widely used at Facebook, Yahoo!, and elsewhere. In 2015, he co-founded (with Chris Re and Feng Niu) Lattice Data, Inc., which is now part of Apple.
John Guttag is the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT. He leads the Computer Science and Artificial Intelligence Laboratory’s Data Driven Medical Research Group. The group works on the application of advanced computational techniques to medicine. Current projects include prediction of adverse medical events, prediction of patient-specific response to therapies, non-invasive monitoring and diagnostic tools, and telemedicine. He has also done research, published, and lectured in the areas of data networking, sports analytics, software defined radios, software engineering, and mechanical theorem proving.
Song Han Song Han is an assistant professor at MIT. Han received his Ph.D. degree from Stanford advised by Professor Bill Dally. His industry experience includes Google Brain (2017-2018), Facebook (2016) and Apple (2013). Han's research focuses on energy-efficient deep learning, at the intersection of machine learning and computer architecture. He proposed Deep Compression that can compress deep neural networks by an order of magnitude without losing the prediction accuracy. He designed EIE: Efficient Inference Engine, a hardware architecture that can perform inference directly on the compressed sparse model, which saves memory bandwidth and results in significant speedup and energy saving. His work has been featured by TheNextPlatform, TechEmergence, Embedded Vision and O’Reilly. He led research efforts in model compression and hardware acceleration for deep learning that won the Best Paper Award at ICLR’16 and the Best Paper Award at FPGA’17. Before joining Stanford, Han graduated from Tsinghua University.
Stefanie Jegelka is an X-Consortium Career Development Assistant Professor in the Department of EECS at MIT. She is a member of the Computer Science and Artificial Intelligence Laboratory and the MIT Statistics and Data Science Center, and an affiliate of IDSS and ORC. Before joining MIT, she was a postdoctoral researcher at UC Berkeley, and obtained her Ph.D. from ETH Zurich and the Max Planck Institute for Intelligent Systems. Jegelka has received an NSF CAREER Award, a DARPA Young Faculty Award, a Google Research Award, the German Pattern Recognition Award and a Best Paper Award at the International Conference for Machine Learning (ICML). Her research interests span the theory and practice of algorithmic machine learning.
Tim Kraska (Co-Director) is an Associate Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory. Currently, his research focuses on building systems for machine learning, and using machine learning for systems. Before joining MIT, Kraska was an Assistant Professor at Brown, spent time at Google Research, and was a PostDoc in the AMPLab at UC Berkeley after he received his Ph.D. from ETH Zurich. Kraska is a 2017 Alfred P. Sloan Research Fellow in computer science and received the 2017 VMware Systems Research Award, an NSF CAREER Award, an Air Force Young Investigator Award, two Very Large Data Bases (VLDB) conference best-demo awards, and a best-paper award from the IEEE International Conference on Data Engineering (ICDE).
Samuel Madden (Co-Director) is a Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory. His research interests include databases, distributed computing, and networking. Research projects include the C-Store column-oriented database system, the CarTel mobile sensor network system, and the Relational Cloud "database-as-a-service." Madden is a leader in the emerging field of large-scale data processing systems, including heading the Intel Science and Technology Center (ISTC) for Big Data, a multi-university collaboration developing new tools for processing massive quantities of data.
David Sontag is an Assistant Professor at MIT. Professor Sontag’s research interests are in machine learning and artificial intelligence. As part of IMES, he leads a research group that aims to transform healthcare through the use of machine learning. Prior to joining MIT, Sontag was an Assistant Professor in Computer Science and Data Science at New York University’s Courant Institute of Mathematical Sciences from 2011 to 2016, and a postdoctoral researcher at Microsoft Research New England from 2010 to 2011. Sontag received the George M. Sprowls Award for outstanding doctoral thesis in Computer Science at MIT in 2010; best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS); faculty awards from Google, Facebook and Adobe; and an NSF CAREER Award. Sontag received a B.A. from the University of California, Berkeley.
Michael Stonebraker is a computer scientist specializing in database research. Through a series of academic prototypes and commercial startups created by Stonebraker and his collaborators over the last 40+ years, Stonebraker's research and products are central to many relational database systems. He also has founded many database companies, including Ingres Corporation, Illustra, Paradigm4, StreamBase Systems, Tamr, Vertica and VoltDB, and served as chief technical officer of Informix. Additionally, he is the winner of many awards, including the ACM Software System Award (1988), IEEE John Von Neumann Medal (2002), and the 2014 A.M. Turing Award (presented in 2015) for "his fundamental contributions to the concepts and practices underlying modern database systems." He has published more than 300 research papers. Stonebraker's recent research has focused on large-scale data processing problems related to data integration, cleaning, and discovery.