Faculty with Computational Neuroscience Research

Erik Sudderth

My research interests span topics traditionally studied in statistics, machine learning, computer vision, and signal processing. Much of my recent work has explored vision systems which segment, recognize, and track objects in complex natural scenes. I believe data-driven, nonparametric Bayesian statistical methods provide a very promising framework to address these problems. My more abstract statistical research is typically inspired by the practical challenges of learning from large, richly structured datasets. Link to website

< back to People