Erik Sudderth
About
Erik B. Sudderth is Professor of Computer Science and Statistics, and Chancellor’s Fellow, at the University of California, Irvine. He directs the UC Irvine Center for Machine Learning and Intelligent Systems, as well as the HPI Research Center in Machine Learning and Data Science at UC Irvine. His research interests include probabilistic graphical models and probabilistic programming, nonparametric Bayesian methods for weakly supervised learning, and applications of statistical machine learning in computer vision and the sciences. Erik was previously an Associate Professor of Computer Science at Brown University, and a postdoctoral scholar at the University of California, Berkeley. He received the Bachelor’s degree (summa cum laude, 1999) in Electrical Engineering from the University of California, San Diego, and the Master’s degree (2002) and Ph.D. degree (2006) in EECS from the Massachusetts Institute of Technology. He received an NSF CAREER award, the ISBA Mitchell Prize, and was named one of “AI’s 10 to Watch” by IEEE Intelligent Systems Magazine.
Research Performance Summary
First Recorded Paper
Adaptive video segmentation: theory and real-time implementation
Year: 1998
Citations: 21
Venue: 1998 Image Understanding Workshop 1
Latest Recorded Paper
A framework for variational inference and data assimilation of soil biogeochemical models using normalizing flows
Year: 2025
Citations: 0
Venue: Journal of Advances in Modeling Earth Systems 17 (8), e2024MS004547
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.