Qingyang Xu, PhD
Welcome! I'm currently a staff AI researcher and manager at Tencent. Previously, I have worked at Meta, DoorDash, and LinkedIn.
I received my Ph.D. in Operations Research at MIT in 2022, where I am fortunate to be advised by Professor Andrew W. Lo, and my undergraduate degree from Stanford.
Research Interests
I am broadly interested in developing advanced techniques in machine learning, statistics and optimization to facilitate the discovery and clinical testing of novel therapeutics.
In particular, I am interested in (1) applying artificial intelligence to predict clinical trial outcomes, (2) optimizing the clinical trial design using multi-armed bandit and reinforcement learning techniques, and (3) devising novel financial strategies to reduce the financial risk of early-stage biomedical investments.
DORADO: Dynamic Optimization of R&D Options. Management Science (2026+)
FairTutor: Equity-Aware Pedagogical LLM Routing for Budget-Constrained AI Tutoring. SIGKDD 2026
Estimating Correlations Between Clinical Trial Outcomes Using Generalised Estimating Equations. Oxford Bulletin of Economics and Statistics (2026)
Predicting Clinical Trial Duration via Statistical and Machine Learning Models. Contemporary Clinical Trials Communications (2025)
Identifying and Mitigating Potential Biases in Predicting Drug Approvals. Drug Safety (2022)
Two-Stage Framework for Seasonal Time Series Forecasting. ICASSP 2021
Bayesian Adaptive Clinical Trials for Anti‐Infective Therapeutics during Epidemic Outbreaks. Harvard Data Science Review (2020)