Welcome to my homepage! I am a postdoctoral research associate at the Machine Learning Department (MLD) of Carnegie Mellon University (CMU), working with Prof. Aarti Singh and Prof. Bryan Wilder. Before joining CMU, I got my PhD in Computer Science at UC Santa Barbara in 2024, advised by Prof. Yu-Xiang Wang. Even before that, I got my B.S. from Tsinghua University in 2019, advised by Prof. Guoqi Li.

I am broadly interested in online learning and decision-making under uncertainty:

• Theoretic foundations:
  • Generative online learning with provable guarantees.
  • Online optimization with non-convex structures.
  • Dynamic pricing algorithms for complex markets.
• Applications:
  • AI-driven decision support for high-stakes healthcare applications.
  • AI-assisted mathematical reasoning and automated theorem proving.

About Generative Online Learning: With the help of Generative AI, now decision-makers may generate new and customized actions beyond learning on existing action sets. Beyond the traditional balance of exploration and exploitation, my works introduce a third dimension –creation– by developing provable algorithms to actively decide when to generate and what to generate given streams of contexts coming on-the-fly.

    I am currently on the 2025–26 job market, and welcome opportunities for academic and industrial research positions. Please check my CV here for more details.

News and Events

Oct 2025.         Glad to receive NeurIPS Top Reviewer for the 3^rd time!

Oct 2025.         Our manuscript “Online Decision Making with Generative Action Sets” is posted on ArXiv. Thanks to my collaborator Vidhi, and my advisors Bryan and Aarti!

Sep 2025.         Two papers will be presented on NeurIPS 2025 MLxOR Workshop. See you at San Diego on Dec 1-7!

Sep 2025.         Our paper “Dynamic Pricing with Adversarially-Censored Demands” is accepted by WINE 2025! Thanks to my collaborators Yining, Xi and Yu-Xiang! The preprint version is posted here on ArXiv. See you at New Brunswick on Dec 7-10!

Apr 2025.         Glad to serve as reviewers for Journal of Machine Learning Research (JMLR) and Mathematics of Operations Research (Math-OR)!

Jan 2025.         Our manuscript “Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach” is posted on ArXiv. Thanks to my collaborators Xuan, Yu-Xiang and Jiashuo!

Jan 2025.         Glad to serve as a reviewer for Journal of the American Statistical Association (JASA) !

Dec 2024.         Glad to serve as an Area Chair in ICML 2025!

Oct 2024.         I’m giving a talk on adversarial dynamic pricing at INFORMS 2024 Annual Meeting. See you at Seattle!

Sep 2024.         Very excited to join CMU MLD as a postdoc!

Aug 2024.         Successfully defended my PhD degree :-D

May 2024.         Our paper “Pricing with Contextual Elasticity and Heteroscedastic Valuation” got accepted by ICML 2024 and selected as spotlight presentation (top 3%)! The preprint version is posted here on ArXiv.