Professional Summary

I am an Associate Professor at HKUST, jointly appointed in the Department of Industrial Engineering and Decision Analytics and the Department of Economics, and the Academic Director of the MSc in FinTech program. I serve as an Associate Editor for several leading journals in the field, including Management Science, Operations Research, Navel Research Logistics, and Queueing Systems.

Education

PhD Management Science and Engineering

Stanford University

MS Financial Mathematics

Stanford University

BS Mathematics

Nankai University

Research Interests

AI Simulation Reinforcement Learning Stochastic Optimization Service Operations Management FinTech
📚 My Research
My research focuses on developing innovative methodologies for data-driven decision-making, leveraging the unprecedented access to data and computational resources. By integrating advanced techniques from simulation, stochastics, and machine learning, I address complex challenges in business operations, finance, and digital economy, while investigating the transformative potential of AI technologies in these domains.
Featured Publications
AI Persuasion, Bayesian Attribution, and Career Concerns of Decision-Makers featured image

AI Persuasion, Bayesian Attribution, and Career Concerns of Decision-Makers

This paper studies AI persuasion by distinguishing between two reasons for disagreement: attention differences, where the AI detects features the decision-maker missed, and …

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Hanzhe Li
Learning to Simulate: Generative Metamodeling via Quantile Regression featured image

Learning to Simulate: Generative Metamodeling via Quantile Regression

Stochastic simulation models effectively capture complex system dynamics but are often too slow for real-time decision-making. Traditional metamodeling techniques learn …

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L. Jeff Hong
SPARKLE: A Nonparametric Approach for Online Decision-Making with High-Dimensional Covariates featured image

SPARKLE: A Nonparametric Approach for Online Decision-Making with High-Dimensional Covariates

Personalized services are central to today's digital economy, and their sequential decisions are often modeled as contextual bandits. Modern applications pose two main challenges: …

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Wenjia Wang
Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning featured image

Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning

Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We …

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Yan Liu
Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management featured image

Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management

LLMs are emerging tools for simulating human behavior in business, economics, and social science, offering a lower‑cost complement to laboratory experiments, field studies, and …

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Runze Zhang