Jason Cheuk Nam Liang
Operations Research Center
Massachusetts Institute of Technology
Email: jcnliang (at) mit (dot) edu
Linkedin profile
About
I am a 5th-year PhD student in the Operations Research Center at MIT, where I am co-advised by Prof. Patrick Jaillet and Prof. Negin Golrezaei.
My research interests lie at the intersection of machine learning, mechanism design, and game theory, with applications in online marketplaces.
Prior to my PhD, I graduated Summa Cum Laude from Columbia University in 2018 with a B.S. degree in Operations Research and Financial Engineering. During my undergraduate studies, I had the pleasure to work with Prof. Adam Elmachtoub, Prof. Garud Iyengar
and Prof. Paul Glasserman.
In winter 2023, I was a PhD researcher at the Market Algorithms Group within Google Research, hosted by Vahab Mirrokni and Yuan Deng.
During 2022 summer, I interned at Citadel as a quantitative researcher, and in 2021 summer at Facebook Research as a researcher in the Economics, Algorithms and Optimization Group within Core Data Science.
Publications
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Multi-channel Autobidding with Budget and ROI Constraints
Journal version in preparation.
Preliminary version accepted to the 40th International Conference on Machine Learning (ICML), 2023
with Yuan Deng, Negin Golrezaei, Patrick Jaillet, and Vahab Mirrokni.
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Bidding and Pricing in Budget and ROI Constrained Markets
Journal version in preparation.
Preliminary version accepted to the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
with Negin Golrezaei, Patrick Jaillet, and Vahab Mirrokni.
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Incentive-aware Contextual Pricing with Non-parametric Market Noise
The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
with Negin Golrezaei and Patrick Jaillet.
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No-regret Learning in Price Competitions under Consumer Reference Effects
Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS), 2020
with Negin Golrezaei and Patrick Jaillet.
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Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
with Adam N. Elmachtoub and Ryan McNellis.
Preprints & Papers under Review