Ziping Xu

Ziping Xu is an Assistant Professor at the University of North Carolina at Chapel Hill's School of Data Science and Society. His primary research focuses on statistical machine learning, reinforcement learning (RL), and their applications in digital interventions, particularly in mobile health. He joined UNC in July 2025 after a postdoc at Harvard University with Susan Murphy and a PhD from the University of Michigan under Ambuj Tewari. His educational background includes a B.S. in data science from Peking University, advised by Song Xi Chen. Xu is actively involved in designing RL components for real mobile health clinical trials, such as ADAPTS-HCT targeting medication adherence in adolescents and young adults undergoing bone marrow transplants.

Ziping’s academic journey includes:

• Bachelor of Science in Data Science, Peking University (2018), advised by Prof. Song Xi Chen.
• PhD in Statistics, University of Michigan (advisor: Prof. Ambuj Tewari).
• Postdoctoral Fellow in Statistics at Harvard University (2023-2025, advised by Prof. Susan Murphy).

His research explores data-driven decision-making approaches, particularly reinforcement learning (RL) and its applications in digital interventions. Key areas include:

• Sample-efficient algorithms leveraging structural information for mobile health.
• Statistical inference for adaptively collected data in clinical trials.
• Transfer learning methodologies addressing domain shifts in healthcare data.
• Designing RL-based solutions for real-world mobile health trials, such as ADAPTS-HCT.

Ziping Xu advises students in his research areas but no specific advisees are listed. Details on grants are not provided in the text. He leads the RL algorithm design for the ADAPTS-HCT clinical trial, collaborating with healthcare teams to enhance medication adherence through digital interventions.