Prof. Devavrat Shah

Andrew (1956) and Erna Viterbi Professor Electrical Engineering and Computer Science
Adjunct Professor, Tata Institute of Fundamental Research
CEO & Co-Founder, Ikigai Labs

Primary DLC

Department of Electrical Engineering and Computer Science

MIT Room: 32-D670

Assistant

Max Taylor
tmax@mit.edu

Areas of Interest and Expertise

Network Algorithms
Stochastic Network Analysis
Gossip and Message-Passing Algorithms
Graphical Models and Network Inference
Scaling Laws for Networks\nStatistical Inference
Blockchain
Machine Learning

Research Summary

Professor Shah’s current research interests are at the interface of Statistical Inference and Social Data Processing. His work has been recognized through prize paper awards in Machine Learning, Operations Research and Computer Science, as well as career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society and 2008 ACM Sigmetrics Rising Star Award. He is a distinguished young alumni of his alma mater IIT Bombay.

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.

Recent Work

  • Video

    10.12-13.22-DigitalTech-Shah

    October 12, 2022Conference Video Duration: 41:57
    Devavrat Shah
    Andrew (1956) and Erna Viterbi Professor, MIT Department of Electrical Engineering and Computer Science

    Learning From Social Data Processing

    May 5, 2017MIT Faculty Feature Duration: 29:28

    Devavrat Shah
    Professor of Electrical Engineering

    Devavrat Shah - 2016 Japan

    January 29, 2016Conference Video Duration: 48:43

    What Do Your Customers (Dis)Like?: Let Their Data Decide

    We live in the era where almost everything we do is recorded somewhere. Naturally such massive amounts of social data contains wealth of information about us. This presents us with a huge opportunity to utilize it for operating businesses efficiently, making meaningful policies and better social living. In this talk, I will discuss how we can utilize social data for predicting preferences of a business's customers accurately. We will discuss such a desirable, scalable data processing system for predicting customer preferences that we have built and deployed. We will describe success stories of this technology in the retail industry.