Jonathan Huggins

A statistician and machine learning researcher who develops scalable Bayesian inference methods with trustworthy uncertainty quantification, even when models are imperfect and inference is approximate. His group builds computational tools applied to large-scale ecological forecasting and to scientific discovery from high-throughput, multi-modal genomic data.