Digital Health Seminar Series: Mengyan Zhang

Mengyan Zhang (University of Bristol)

Learning to Decide Under Uncertainty: From Bandit Theory to Health Applications

How can AI systems make reliable sequential decisions when interacting with dynamic environments under limited budgets? Sequential decision-making frameworks such as multi-armed bandits, reinforcement learning, and active learning provide principled foundations for addressing these challenges in health and science.

In this talk, Dr Zhang will present sequential decision problems arising in biological sequence modelling and discovery, as well as disease surveillance using graph-based active learning. These examples illustrate how learning under uncertainty can guide data collection, experimentation, and intervention in resource-constrained health contexts. She will conclude by outlining open challenges and future research directions at the intersection of sequential decision-making and digital health.

Speaker bio:

Dr. Mengyan Zhang is a Lecturer in the School of Computer Science at the University of Bristol. She is also a visiting researcher at the University of Oxford and a member of the Machine Learning and Global Health Network. Prior to joining Bristol, she was a postdoctoral researcher at the University of Oxford. 

She received her PhD from the Australian National University and was affiliated with Data61, CSIRO. Her research focuses on sequential decision making in machine learning, including multi-armed bandits, reinforcement learning and active learning, with applications in areas such as disease surveillance, synthetic biology, and public policy.

Please note: this seminar will be recorded and shared on YouTube.