I build reliable AI that learns and makes decisions under uncertainty, incomplete information, and heterogeneous real-world data — with a focus on healthcare.
AI methods that stay reliable when data are heterogeneous, incomplete, and high-stakes.
Uncertainty quantification and fairness — models that know when they don't know and treat people equitably.
Learning across images, records, and time series, and building generative models that can be trusted.
Medical imaging, longitudinal health data, and Alzheimer's disease and related dementias (AD/ADRD).
I'm looking for highly self-motivated Ph.D. students and interns. Email your CV, transcript, and anything else you'd like to share.
GRE is required. Please apply only if you have taken the GRE or plan to take it soon.


