Devavrat Shah

Devavrat Shah is the Andrew (1956) and Erna Viterbi Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and is a member of the Institute for Data, Systems and Society (IDSS), Laboratory for Information and Decision Systems (LIDS), and the Statistics and Data Science Center (SDSC). His research focuses on statistical inference and stochastic networks, and 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 networks, his work spans a range of areas across electrical engineering, computer science and operations research. He earned a BS in computer science and engineering from the Indian Institute of Technology, and a PhD in computer science from Stanford University.

Selected Publications

• Jadbabaie, A., Makur, A., & Shah, D. (2024). Gradient-based empirical risk minimization using local polynomial regression. Stochastic Systems, 14(4), 363–402.
• Alur, R., Raghavan, M., & Shah, D. (2024). Human expertise in algorithmic prediction. In Advances in Neural Information Processing Systems 37 (NeurIPS).
• Han, J. X., Miller, A. C., Watkins, S. C., Winship, C., Christia, F., & Shah, D. (2024). A causal framework to evaluate racial bias in law enforcement systems. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7(1), 562–572.

Media

• May 27, 2025: MIT News, Building networks of data science talent
• November 4, 2024: MIT News, Empowering systemic racism research at MIT and beyond
• November 9, 2023: MIT News, Explained: Generative AI