News

• NFSC：我国学者在信息增强型广告机制研究中取得进展。

• Management Science 官方 LinkedIn： Why Live-Streaming Adverts Are the New Static Adverts.

• InsuranceBusiness News： Banning Loyalty Penalties in Insurance Could Backfire, Expert Warns.

• 《香港经济日报》：投資初創企業，應如何配置資源？

• 《FT 中文网》：算法孪生：从“大数据杀熟”到隐性合谋.

研究兴趣：平台生态、拍卖与机制设计、离散选择/需求建模、推荐与定价算法、AI+决策、能源市场。

Awards

• Second Place, Best Conference Paper Award, National Conference on Supply Chain and Operations Management, 2025

• Third Prize, Best Paper Award, POMS-China, 2025

• Honorable Mention, Best Paper Award, POMS-China, 2025

• Finalist, INFORMS Service Science Best Student Paper Competition, 2024

• Honorable Mention, Best Paper Award, POMS-HK, 2024

• First Place, Best Paper Award, National Conference on Supply Chain and Operations Management, 2023

• Second Place, Best Paper Award, POMS-China, 2022

• Honorable Mention, Best Paper Award, POMS-HK, 2022

Research Interests 

My recent work focuses on how AI reshapes demand analytics, operational decision-making, and platform regulation.

1. Demand Estimation. I develop nonparametric and data-driven methods for demand inference under complex and heterogeneous data. I also explore how large language models, transfer learning, and data augmentation can strengthen demand estimation in real applications. These works are still in progress—stay tuned!

2. Operations Optimization. I design tractable optimization surrogates with theoretical guarantees for recommendation and pricing decisions, and leverage AI architectures to accelerate large-scale decision making. 

   • “Assortment Optimization with Downward Feasibility: Efficient Heuristics Based on Independent Demands” and “Prophet Inequalities with Stochastic Choices” propose linear surrogates to solve general recommendation problems.

   • “RCS Approximations for Assortment Optimization with Sequential Consumer Behavior” uses the simple RCS model to solve sequential assortment problems.

   • “From Small to Large: A Graph Convolutional Network Approach for Solving Assortment Optimization Problems” uses GCN models to accelerate assortment optimization.

   • “Menu Optimization for Meal Delivery Platforms” improves the operational performance of a major Canadian food-delivery company.

3. Platform Regulation. Using game-theoretic modeling, I study how fairness, privacy protection, live-streaming, and AI-generated content shape platform competition, cooperation, and ecosystem stability. I am also exploring how LLM-based simulations can be used to evaluate and enhance platform policies and ecosystem design.

   • “Fairness Regulation of Prices in Competitive Markets” and "Regulating Discriminatory Pricing in the Presence of Tacit Collusion" show how fairness constraints reshape competition and tacit collusion.

   • “Position Auctions with Endogenous Product Information: Why Live-Streaming Advertising Is Thriving” and “Sponsored Search with AI-Generated Ad Content” reveal how flexible information provision changes advertising markets.

Full publication lists can be found on my Google Scholar page.