My main research areas are computer vision, statistical inference, and machine learning. I focus on developing new practical and mathematically-principled computational tools for analysis of stochastic high-dimensional real-world signals. I'm interested in tools that provide interpretability and uncertainty quantification, that scale gracefully with the data's size, and that adapt model complexity to the data. I'm particularly interested in Bayesian or geometric methods and problems such as unsupervised learning, motion analysis, segmentation, statistical image models, and deep learning.

Recent or current research topics (with plenty of overlap)

Past research topics