Shreyas Sekar
Bio
Shreyas Sekar is an Assistant Professor at the Department of Management, University of Toronto Scarborough with a cross-appointment in the Operations Management and Statistics Area at the Rotman School of Management. Prior to this, he was a Postdoctoral Fellow at the Lab for Innovation Science at the Harvard Business School. His research uses prescriptive, data-driven methodologies to tackle operational challenges arising in online marketplaces, in particular e-commerce platforms. To address such questions, Shreyas draws upon tools from diverse domains including online learning, game theory, combinatorial optimization, and mechanism design. Shreyas received his Ph.D in 2017 at the Rensselaer Polytechnic Institute, where he received the Robert McNaughton prize for the best graduate dissertation in Computer Science.
Selected Publications - Papers
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Disintermediation in Online Platforms: Price and Reputation Effects.
Auyon Siddiq
In Preparation
2022
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Optimal Subscriptions for Ridesharing Platforms
Ben Berger, Hongyao Ma, David Parkes, Shreyas Sekar
In Preparation
2022
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Negin Golrezaei, Vahideh Manshadi, Jon Schneider, Shreyas Sekar
Forthcoming, Operations Research
2022
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Kris Ferreira, Sunanda Parthasarathy, Shreyas Sekar
Management Science
Issue:Special Issue on Prescriptive Data-Driven Analytics (Volume 68, Issue 3)
2022
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Shreyas Sekar, Milan Vojnovic, SeYoung Yun
Management Science
Issue:Vol 67, Issue 2
2021
Pages: 661-1328, iii-iv
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Pan Li, Shreyas Sekar, Baosen Zhang
IEEE Transactions on Sustainable Computing
2019
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Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff, Baosen Zhang
IEEE Transactions on Automatic Control
Issue:Vol 65, Issue 11
2019
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Elliot Anshelevich, Koushik Kar, Shreyas Sekar
ACM Transactions on Economics and Computation
Issue:Vol.5, Issue 3
2017
Pages: pp 1–42
Featured Work
Currently, I am excited by two key questions pertaining to online platforms:
- How can we design optimal, data-driven policies when the underlying data is corrupted due to strategic manipulation or fraudulent behaviour? This is a growing concern among digital platforms due to the rise of paid reviews, fake users, collusion, etc. In recent work, my colleagues and I proposed an online learning algorithm for e-commerce platforms to identify the optimal product ranking even when the underlying data is corrupted by fake users.
Media Coverage: Fake online reviews are everywhere. Meet the UTSC prof who’s developing strategies to stop them
- How should digital platforms design information architectures, i.e., make decisions on the quality, quantity, and order in which information is presented, given that consumers have finite attention online? In a series of papers as well ongoing work, I have sought this address this challenge in a variety of settings:
--- Product Ranking on e-Commerce Platforms (Collaboration with Wayfair.com)
--- Travel Time information to drivers on the road
--- Price disclosure in energy markets
--- Presenting information to participants in contests (ongoing)
Research and Teaching Interests
Data-Driven Analytics, Revenue Management and Pricing, Digital Marketplaces and Market Design, Online Learning, Information Economics, Decision Making under Uncertainty.
Honors and Awards
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2021
Keynote Speaker, Hashtag Redcarpet Conference, University of Toronto Scarborough
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2021
TD MDAL Grant, TD Management Data and Analytics Lab
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2021
Research Competitiveness Fund, University of Toronto Scarborough
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2018
Finalist, Best paper award, ACM e-Energy Conference
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2017
Robert McNoughton Prize for Best Graduate Dissertation in Computer Science, Rensselaer Polytechnic Insitute
Professional Affiliations/Memberships
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Schwartz Reisman Institute for Technology and Society
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Better AI Objectives Group, Vector Institute for Artificial Intelligence
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Mechanism Design for Social Good