Welcome!

I am Hansheng, an Assistant Professor of Operations Management and Statistics at the Joseph L. Rotman School of Management, University of Toronto, and a faculty affiliate at the School of Cities.

My research develops methods and insights for AI-powered and data-driven decision-making under uncertainty. A recurring theme in my work is how predictions can be translated into reliable operational decisions while accounting for incomplete information, dynamic environments, and human preferences, with applications in supply chains, logistics, retailing, pricing, and sustainability.

Before joining the University of Toronto, I received my Ph.D. in Industrial Engineering and Operations Research (Ph.D. minor in Statistics) from the University of California, Berkeley, advised by Max Shen and Aditya Guntuboyina. Prior to Berkeley, I received my B.S. in Mathematics from USTC.

News

• 07/2026 Received the Connaught New Researcher Award for research on AI agents in markets and operations.
• 07/2026 Talks at the MSOM Conference in Boston and the RMP Conference in Ann Arbor.
• 05/2026 Paper Learning in MDPs for Network Inventory Management accepted to AISTATS; extended abstract of Conformal Robustness in Prediction-Driven Decision-Making accepted to the AISTATS Calibration for Modern AI Workshop.
• 11/2025 Received a Management Data and Analytics Lab grant with PhD student Guan Wang for research on smart urban planning with foot traffic data.
• 10/2025 Talk at INFORMS Annual Meeting in Atlanta about Designing Surprise Bags for Surplus Foods.
• 07/2025 Paper Spatial Supply Repositioning with Censored Demand Data received Second Place in the CSAMSE Best Paper Award Competition.
• 07/2025 Talks at INFORMS RMP Conference at Columbia Business School, INFORMS International Meeting at Singapore, CSAMSE Annual Conference at Chengdu.
• 02/2025 Talks at INFORMS Computing Society Conference and PKU Guanghua.
• 01/2025 Delighted to host Andrea Li (Too Good To Go) for an Industry Practitioner seminar at Rotman.
• 12/2024 Glad to serve in the Franz Edelman Award Committee.
• 11/2024 Had a great time attending the Economics of QIT workshop at University of Southern California.
• 10/2024 New working paper "Designing Surprise Bags for Surplus Foods" explores optimal strategies for stores in balancing revenue, customer satisfaction, and sustainability in food waste reduction.
• 09/2024 Offering a new Rotman PhD topic course on nonparametric models and methods for data-driven decision-making in Fall 2024.
• 07/2024 I gave a 60-minute talk at Rotman Young Scholar Seminar [Video Recording].
• 06/2024 Presenting in MSOM Conference at Minneapolis, RMP Conference at Los Angeles, and ISMP Conference at Montreal. See you there!
• 04/2024 I am excited and grateful to receive the NSERC Discovery Grant!
• 02/2024 Congratulations to my student mentee Zeqi Ye for being admitted to the Northwestern IEMS PhD Program!
• 01/2024 Paper on Smoothness Adaptive Dynamic Pricing is accepted to AISTATS 2024.
• 01/2024 Teaching RSM270 Operations Management in Winter 2024.
• 12/2023 Approved funding from the CANSSI Ontario Multidisciplinary Doctoral (Mdoc) Training Program.
• 12/2023 Talks at USTC School of Management and POMS-HK.
• 10/2023 I will be at INFORMS Annual Meeting 2023 at Phoenix (related sessions).
• 08/2023 Invited talks at MOPTA 2023 ("Potential Energy Advantage of Quantum Economy") and YinzOR 2023 ("Intertemporal Pricing in the Presence of Consumer Behaviors").
• 06/2023 Honored to receive the Second Place in Doctoral Dissertation Award Competition, Conference on SCM in the Post-Pandemic and AI Age. See the recording for a brief 20-min introduction.
• 01/2023 Excited to teach as an instructor for the first time! Welcome to STAT 153: Introduction to Time Series.
• 10/2022 Our paper "Quantum Computing Methods for Supply Chain Management" is published on the Proceeding of 2022 IEEE/ACM 7th Symposium on Edge Computing - Workshop on Quantum Computing!
• 08/2022 I won a prize in the flash talk competition at YinzOR 2022 held by Carnegie Mellon University!

Info

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