Ziping Xu

Ziping Xu is a Research Fellow in the Department of Statistics at Harvard University, working under Professor Susan Murphy. His research focuses on Statistical Reinforcement Learning, Transfer Learning, and Multitask Learning, with applications in mobile health and digital interventions.

Research Focus: Xu develops sample-efficient algorithms for adaptive decision-making systems, statistical methods for inference in adaptive data collection, and transfer learning approaches for domain shifts. He leads RL algorithm design for clinical trials like ADAPTS-HCT, targeting medication adherence in young transplant patients.

Publications: His 15 most recent publications demonstrate consistent focus on reinforcement learning optimizations for healthcare applications, algorithmic improvements for non-stationary environments, and theoretical advances in multitask learning. Recurrent themes include medication adherence systems, cryo-EM imaging optimization, and bandit algorithms for personalized medicine.