Research

Many quantities that matter in biology and medicine are hidden: a protein’s conformational ensemble, the tissue properties behind an image, a patient’s physiological state. What we observe is only a measurement of them. My research develops AI that combines learned priors with models of the measurement process to infer these hidden states, determine what the evidence supports, and decide what to measure next. I pursue this across molecules, tissues, and human physiology, with uncertainty estimation as the common thread.

Structural Biology

Inferring protein structure and conformational ensembles from experimental measurements.

Machine Learning for Health

Learning from wearable, physiological, and omics data to model human health.

Computational Imaging

Co-designing acquisition hardware and reconstruction algorithms with learning.

Uncertainty Estimation

Quantile-based and distributional methods for calibrated, multivariate uncertainty.

For the complete list, see publications, which can be filtered by field.