Industrial Engineering and Operations Research
Shipra Agrawal’s research spans several areas of optimization and machine learning, including data-driven optimization under partial, uncertain, and online inputs, and related concepts in learning, namely multi-armed bandits, online learning, and reinforcement learning. She is also interested in prediction markets and game theory. Application areas of her interests include internet advertising, recommendation systems, revenue management, and resource allocation problems.
- Researcher, Microsoft Research (MSR), India, 2013-2015
- Postdoctoral researcher, Microsoft Research (MSR), India, 2011-2013
- Member of Research Staff, Bell Labs Alcatel-Lucent, India, Dec 2004-Aug 2006
- Software Engineer, Yahoo! Software India Pvt. Ltd., India, Aug 2004-Dec 2004
- Member of ACM Future of Computing Academy
- Associate editor for Management Science journal
- Selected as an inaugural member of the ACM Future of Computing Academy (FCA). Announced April 2017.
- Provosts Grant for Junior Faculty who Contribute to the Diversity Goals of the University (2016-2017)
- S. Agrawal, N. Goyal, "Near-optimal regret bounds for Thompson Sampling" Forthcoming in the Journal of ACM (2017).
- S. Agrawal, V. Avandhanula, V. Goyal, A. Zeevi, "Thompson Sampling for MNL-bandit". Conference on Learning Theory (COLT), 2017.
- S. Agrawal, V. Avandhanula, V. Goyal, A. Zeevi, "An Exploration-Exploitation Approach for Assortment Selection". ACM conference on Economics and Computation (EC) 2016.
- S. Agrawal, N. R. Devanur, "Fast algorithms for online stochastic convex programming". In Proceedings of the 21st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015.
- S. Agrawal, N. R. Devanur, "Bandits with concave rewards and convex knapsacks". In Proceedings of the 15th ACM Conference on Electronic Commerce (EC), 2014.
- S. Agrawal, Z. Wang and Y. Ye, "A Dynamic Near-Optimal Algorithm for Online Linear Programming". Operations Research 62:876-890 (2014).
- S. Agrawal, N. Goyal, "Thompson Sampling for contextual bandits with linear payoffs". In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
- S. Agrawal, N. Goyal, "Analysis of Thompson Sampling for the multi-armed bandit problem". In Proceedings of the 25th Annual Conference on Learning Theory (COLT), 2012.
- S. Agrawal, Y. Ding, A. Saberi, and Y. Ye, "Price of Correlations in Stochastic Optimization". Operations Research 60:243-248 (2012).
- S. Agrawal, E. Delage, M. Peters, Z. Wang, and Y. Ye, "A Unified Framework for Dynamic Prediction Market Design". Operations Research 59:3:550-568 (2011).
A complete updated list is available at http://www.columbia.edu/~sa3305/