Erik B. Sudderth, Statistical Computation & Perception

I am an Associate Professor of Computer Science and Statistics at the University of California, Irvine. I previously spent seven great years on the faculty at Brown University, where I remain an Adjunct Associate Professor of Computer Science. My Learning, Inference, & Vision Group develops statistical methods for scalable machine learning, with applications in artificial intelligence, vision, and the natural and social sciences. Particular areas of expertise include:

Machine Learning
graphical models, Bayesian nonparametrics, approximate inference
Computer Vision
object recognition & scene understanding, segmentation, motion & tracking
Signal Processing
nonlinear dynamical systems, image & video analysis, multiscale models

See my CVPR tutorial for an overview of Bayesian nonparametrics in computer vision. For a tutorial introduction to probabilistic modeling and approximate inference, see the background chapter of my doctoral thesis, advised by Professors Alan Willsky and William Freeman at MIT EECS. My postdoctoral research at Berkeley EECS was advised by Professors Michael Jordan and Stuart Russell.

For more information: bio · curriculum vitæ · research projects & code · publications & lectures

Research Highlights

Editorial Highlights

Erik Sudderth
Erik B. Sudderth
P: (949) 824-8169

Office: Donald Bren Hall 4028
Mailing Address:
University of California, Irvine
School of Information & Computer Sciences
Irvine, CA 92697-3435
UC Irvine