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.

For more information: 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