Ziping Xu

Research Interests

My research interests focus on exploring new frontiers of data-driven decision-making approaches (e.g., Reinforcement Learning) with applications in the digital intervention domain e.g., mobile health. A few highlights of my works are:

• Sample-Efficient Online Algorithm Design: Sample efficiency is crucial in data scarce domains like mobile health. I develop algorithms that leverage structural information to improve sample efficiency [I,II,III].


• Statistical Inference for Adaptively Collected Data: Adaptive experimental designs, commonly used in mobile health, introduce dependencies across time and users, introducing challenges to existing statistical inference methods. I develop new approaches that remain valid in these challenging settings [I,II].


• Transfer Learning/Multitask Learning: Due to limited sample sizes in mobile health, transferring knowledge from existing datasets is often necessary. However, significant domain shifts, driven by differences in participant demographics, intervention designs, or societal changes, complicate this process. I design transfer learning approaches that account for such shifts, enabling more robust and effective learning [I,II,III,IV,V].