Hi, I'm Ping-Han

I am a PhD candidate in Statistics at ASU School of Mathematical and Statistical Sciences, with a concentration in machine learning (ML) and design of experiments (DOE). My current work develops Bayesian sequential optimal design for functional/longitudinal data, with an emphasis on decision-theoretic criteria, scalable posterior computation, and uncertainty-aware adaptivity. I build methods that learn sampling policies for repeated-measures and design algorithms for high-dimensional trajectories under cost constraints. I am looking forward to expanding my horizons with various creative research projects.