Erik B. Sudderth, Statistical Computation @ Brown University

I am an Assistant Professor of Computer Science at Brown University. 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: (401) 863-7660
F: (401) 863-7657

Office: CIT Room 555
Mailing Address:
Dept. of Computer Science
115 Waterman Street
Brown University, Box 1910
Providence, RI 02912
Brown University