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

• Alomar, A., Hamadanian, P., Nasr-Esfahany, A., Agarwal, A., Alizadeh, M., Shah, D. (2023). CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation. Symposium on Networked Systems Design and Implementation (USENIX).
• Cen, S. H., Shah, D. (2022). Regret, stability & fairness in matching markets with bandit learners. International Conference on Artificial Intelligence and Statistics (AISTATS).
• Agarwal, A., Shah, D., Shen, D. (2022). Causal Imputation via Synthetic Interventions. Conference on Causal Learning and Reasoning (CLeaR).

Media

• August 19, 2020: EdExLive, Great Learning launches online bootcamp in Data Science in collaboration with MIT faculty.
• May 15, 2020: MIT News, A data-driven response to a pandemic.
• December 9, 2019: MIT News, Model beats Wall Street analysts in forecasting business financials.