Shipra Agrawal
Date

August 2023

ContactInformation

email:
web:address:

ResearchInterests

Sequential decision making under uncertainty, Multi-armed bandits, Reinforcement
learning, Online learning, Online optimization, Game theory and Mechanism design.

Education

Stanford University, Stanford, CA
Ph.D., Computer ScienceSep 2006 - June 2011 Thesis title: “Optimization under uncertainty: Bounding the correlation gap”

sa3305@columbia.edu
http://www.columbia.edu/~sa3305/
Columbia University
423 Mudd Building
500 West 120th Street
New York, NY 10027

 Advisor: Yinyu Ye, Management Science and Engineering (MS&E)

EmploymentIndian Institute of Science, Bangalore, India
M.E., Computer Science and Automation

Aug 2002 - Apr 2004

M.B.M. Engineering College, Jodhpur, India
B.E., Computer Science and Engineering

Aug 1998 - Apr 2002

Columbia University, New York, NY
Associate Professor
Department of Industrial Engineering and Operations Research
Affiliate, Department of Computer Science
Affiliate, Data Science InstituteSep 2015 - present

Google, New York, NY
Visiting Faculty Researcher

Jan 2022 - Jan 2023

Amazon, Seattle, WAAmazon Scholar

Jan 2019 - Jan 2021

Microsoft Research (MSR), Bangalore, India
Researcher

Jul 2013 - Aug 2015

Microsoft Research (MSR), Bangalore, India
Postdoctoral researcher

Aug 2011 - Jun 2013

IBM Almaden Research Center, San Jose, CAResearch Intern

Jun 2008 - Sep 2008

Bell Labs Alcatel-Lucent, Bangalore, India
Member of Research Staff

Dec 2004 - Aug 2006

Yahoo! Software India Pvt. Ltd., Bangalore, India
Software EngineerAug 2004 - Dec 2004

Publications(In all the publications listed below, the author ordering is alphabetical and indicatesequal contribution, except when the first author is indicated by *)(Underline indicates student coauthors)
Journal publications
1. S. Agrawal, R. Jia, “Optimistic Posterior Sampling for Reinforcement Learning:worst-case regret bounds”, Mathematics of Operations Research. Volume 48, Issue1, February 2023.
- Conference version in Neural Information Processing Systems (NeurIPS), 2017
- Neurips Spotlight paper: ∼4% of 3240 submissions.
2. S. Agrawal, R. Jia, “Learning in Structured MDPs with Convex Cost Functions:improved regret bounds for inventory management”. Operations Research. Volume
70 Issue 3, May-June 2022.
- Conference version in ACM conference on Economics and Computation (EC) 2019.
3. S. Agrawal, V. Avadhanula, V. Goyal, A. Zeevi, “MNL-Bandit: A Dynamic Learning Approach to Assortment Selection”. Operations Research, 67(5):1209-1502 (2019).
- Conference version in ACM conference on Economics and Computation (EC) 2016
4. S. Agrawal, N. R. Devanur, “Bandits with Global Convex Constraints and Objective”. Operations Research 67(5):1486-1502 (2019).
- Invited to the special issue of ACM Transactions on Economics and Computation(TEAC) featuring selected papers from EC 2014 (invitation declined).
- Conference version in ACM conference on Economics and Computation (EC) 2014
5. S. Agrawal, “Recent Advances in Multiarmed Bandits for Sequential Decision
Making”, INFORMS TutORials in Operations Research, Operations Research &Management Science in the Age of Analytics, Pages 167–188, October 2019.
6. S. Agrawal, N. Goyal, “Near-optimal regret bounds for Thompson Sampling”.
Journal of the ACM (JACM), Volume 64 Issue 5, October 2017.
7. S. Agrawal, Z. Wang, and Y. Ye, “A Dynamic Near-Optimal Algorithm for Online
Linear Programming”. Operations Research 62:876-890 (2014).
8. S. Agrawal, Y. Ding, A. Saberi, and Y. Ye, “Price of Correlations in StochasticOptimization”. Operations Research 60:243-248 (2012).
- Conference version in ACM-SIAM Symposium on Discrete Algorithms SODA’10
9. 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).
- Conference version in ACM conference on Economics and Computation (EC) 2009
10. S. Agrawal, N. Megiddo, and B. Armbruster, “Equilibrium in Prediction Markets
with Buyers and Sellers”. Economic Letters 109:46-49 (2010).
11. S. Agrawal, J.R. Haritsa, and B. A. Prakash, “FRAPP: A Framework for HighAccuracy Privacy-Preserving Mining”. Data Mining and Knowledge Discovery Journal 18:101-139 (2009).
12. S. Agrawal, C. N. Kanthi, K. V. M. Naidu, J. Ramamirtham, R. Rastogi, S.
Satkin, and A. Srinivasan, “Monitoring Infrastructure for Converged Networks andServices”. Bell Labs Technical Journal 12(2): 63-77 (2007).

Journal papers under review
 S. Agrawal, V. Avadhanula, V. Goyal, A. Zeevi, “Thompson Sampling for theMNL-Bandit”, Accepted to Appear, Mathematics of Operations Research.
 T. Kocák*, M. Valko, R. Munos, S. Agrawal, “Spectral Thompson Sampling”.
Journal of Machine Learning Research. Accepted conditional on minor revision.

Peer-reviewed conference papers (not superseded by journal publications)13. S Yin*, S. Agrawal, A Zeevi, Online Allocation and Learning in the Presence ofStrategic Agents, Neural Information Processing Systems (Neurips) 2022.
14. S. R. Putta*, S. Agrawal, Scale Free Adversarial Multi Armed Bandits, The 33rd
International Conference on Algorithmic Learning Theory (ALT) 2022.
15. S. Agrawal, S. Yin, A. Zeevi, Dynamic Pricing and Learning under Bass Model,
ACM conference on Economics and Computation (EC) 2021.
16. S. Agrawal, E. Balkanski, V. Mirrokni, B. Sivan, Dynamic First Price Auctions
Robust to Heterogeneous Buyers ACM conference on Economics and Computation(EC) 2021.
17. Y. Tang*, S. Agrawal, Y. Faenza, “Reinforcement Learning for Integer Programming: Learning to Cut”. International Conference on Machine Learning (ICML)
2020.
18. S. Agrawal, J. Sethuraman, X. Zhang, “On Optimal Ordering in the Optimal
Stopping Problem”, ACM conference on Economics and Computation (EC) 2020.
19. Y. Tang*, S. Agrawal, “Discretizing Continuous Action Space for On-Policy Optimization”, AAAI Conference on Artificial Intelligence (AAAI), 2020.
20. S. Agrawal, M. Shadravan, C. Stein, “Submodular Secretary Problem with Shortlists”, Innovations in Theoretical Computer Science (ITCS), 2019.
21. S. Agrawal, C. Daskalakis, V. Mirrokni, B. Sivan, “Robust Repeated Auctions
under Heterogeneous Buyer Behavior”, ACM conference on Economics and Computation (EC), 201822. S. Agrawal, V. Mirrokni, M. Zadimoghaddam, “Proportional Allocation: Simple,Distributed, and Diverse Matching with High Entropy”, International conference
on Machine Learning (ICML), 2018.
23. C. Pike-Burke*, S. Agrawal, S. Grunewalder, C. Szepesvari, “Bandits with Delayed, Aggregated Anonymous Feedback”, International conference on MachineLearning (ICML), 2018.
24. Y. Tang*, S. Agrawal, “Exploration by Distributional Reinforcement Learning”,
International Joint Conference on Artificial Intelligence (IJCAI), 2018.
25. S. Agrawal, V. Avadhanula, V. Goyal, A. Zeevi, “Thompson Sampling for the
MNL-Bandit”, Conference in Learning Theory (COLT) 2017.
26. S. Agrawal, N. R. Devanur, “Linear Contextual Bandits with Knapsacks”. Neural
Information Processing Systems (NeurIPS), 2016.
27. S. Agrawal, N. R. Devanur, L. Li, “Contextual Bandits with Knapsacks”. Conference on Learning Theory (COLT), 2016.
28. S. Agrawal, N. R. Devanur, “Fast Algorithms for Online Stochastic Convex Programming”. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015.
- Invited to the special issue of ACM Transactions on Algorithms featuring selectedpapers from SODA 2015 (invitation declined).
29. T. Kocák*, M. Valko, R. Munos, S. Agrawal, “Spectral Thompson Sampling”.
AAAI Conference on Artificial Intelligence (AAAI), 2014.

30. S. Agrawal, N. Goyal, “Thompson Sampling for Contextual Bandits with Linear
Payoffs”. International Conference on Machine Learning (ICML), 2013.
31. S. Agrawal, N. Goyal, “Further Optimal Regret Bounds for Thompson Sampling”,
International Conference on Artificial Intelligence and Statistics (AISTATS), 2013.
32. S. Agrawal, N. Goyal, “Analysis of Thompson Sampling for the Multi-armed Bandit Problem”. Conference on Learning Theory (COLT), 2012.
33. S. Agrawal, Z. Wang, and Y. Ye, ‘Parimutuel Betting on Permutations”. International Workshop On Internet And Network Economics (WINE), 2008.
34. S. Agrawal, K.V.M. Naidu, and R. Rastogi, “Diagnosing Link-Level Anomalies
Using Passive Probes”. IEEE Conference on Computer Communications (INFOCOM), 2007.
35. S. Agrawal, S. Deb, K.V.M. Naidu, and R. Rastogi, “Efficient Detection of Distributed Constraint Violations”. Short paper. International Conference on DataEngineering (ICDE), 2007.
36. S. Agrawal, P.P.S. Narayan, J. Ramamirtham, R. Rastogi, M. Smith, K. Swanson, and M. Thottan, “VoIP Service Quality Monitoring using Active and PassiveProbes”. International Conference on COMmunication System softWAre and MiddlewaRE (COMSWARE), 2006.
37. S. Agrawal, J.R. Haritsa, “A Framework for High-Accuracy Privacy-Preserving
Mining”. International Conference on Data Engineering (ICDE), 2005.
38. S. Agrawal, V. Krishnan, and J.R. Haritsa, “On Addressing Efficiency Concerns
in Privacy-Preserving Mining”. International Conference on Database Systems for
Advanced Applications (DASFAA), 2004.
Awards andHonors

 Elected to COLT (Conference on Learning Theory) Board of Directors (aka ’COLTsteering committee’).
 Cyrus Derman Assistant Professorship of Industrial Engineering and Operations Research, Columbia University (effective January 1, 2020 through December 31, 2022).
 NSF CAREER Award 2019-2023.
 Google Faculty Research Award 2017-2018.
 Amazon Faculty Research Award 2017-2018.
 Honorable mention, INFORMS Junior Faculty Interest Group (JFIG) prize 2017.
 Selected as an inaugural member of the ACM Future of Computing Academy (FCA).
Announced April 2017.

Recent activites  General chair for Conference on Learning Theory (COLT) 2024. (co-chair AaronRoth).
 Member and participant, DARPA Information Science & Technology (ISAT) study
group, since July 2023.
 Member, Tucker Prize committee for ISMP 2024.
 General chair for ALT 2023 in Singapore, Feb 20-23, 2023. (co-chair FrancescoOrabona).
 Organizer, Fall’22 semester on Data-Driven Decision Processes at Simons Institute,Berkeley, CA. Aug. 17 – Dec. 16, 2022. (co-organizers Thodoris Lykouris, Siddhartha Banerjee, Shuchi Chawla)