معرفی
Kelly W. Zhang is an Assistant Professor at Imperial College London's Mathematics Department (statistics section) and a faculty member in the I-X interdisciplinary AI initiative. Her research focuses on adaptive experimentation, reinforcement learning, and statistical inference with applications in healthcare and clinical trials. She holds a PhD from Harvard University and was a Postdoctoral Fellow at Columbia Business School. Notable awards include the Siebel Scholar (2023) and NSF Graduate Fellowship.
Education: PhD in Computer Science from Harvard University (2023), advised by Susan Murphy and Lucas Janson; internships at Apple, Facebook AI, and eBay. Her work bridges statistical theory and practical applications in digital health interventions, with deployments in oral health and cannabis use trials.
Research Interests:
- Reinforcement Learning Algorithms for Digital Interventions
- Statistical Methods in Adaptive Experimentation
- Clinical Trial Design and Monitoring
- Machine Learning Theory and Applications
Awards:
- Siebel Scholar (2023)
- NSF Graduate Fellowship (PhD support)
- Presentations at NeurIPS 2021/2020 and Econometric Society Conference 2024
Grants and Funding: Her work has been supported through interdisciplinary initiatives at Imperial College and prior fellowships. She co-leads sessions on statistical reinforcement learning at conferences like INFORMS and JSM.
Professional Activities: Co-organizer of sessions at INFORMS 2025, IMS-Bernoulli 2024, and workshops on Deployable RL. Active in academic outreach, including speaking at Amazon Berlin's StatML workshop.
