Devavrat Shah is a Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology since 2005. He is a member of the Laboratory for Information and Decision Sciences (LIDS) and the Institute for Data, Systems and Society (IDSS). He directs the Statistics and Data Science Center (SDSC). He is a visiting Adjunct Professor at Tata Institute of Fundamental Research (TIFR) since March 2018.
His research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, and algorithms for social data processing including ranking, recommendations and crowdsourcing. Within the broad context of networks, his work spans a range of areas across electrical engineering, computer science and operations research.
Shah received a bachelor’s degree in computer science and engineering from the Indian Institute of Technology in Bombay, where he received the Presidents of India Gold Medal, which is awarded to the best graduating student across all engineering disciplines. He received a PhD in computer science from Stanford University with George B. Dantzig Dissertation Award from Institute for Operations Research and the Management Sciences (INFORMS).
His work has received broad recognition including Rising Star Award from the Association for Computing Machinery (ACM) Special Interest Group for the computer systems performance evaluation community (SIGMETRICS), the Erlang Prize from the Applied Probability Society of INFORMS in addition to paper prize awards including the Best Publication Award from the Applied Probability Society of INFORMS, Best Paper Award from Manufacturing and Service Operations Management Society of INFORMS, NIPS Best Paper Award and ACM SIGMETRICS Best Paper Award. He received NSF CAREER Award and he is distinguished young alumni of his alma mater IIT Bombay. He founded the machine learning start-up Celect, Inc. which helps retailer with optimizing inventory by accurate demand forecasting.