Highlights
January 2012 This spring I'm teaching Intro to Machine Learning.
December 2011 NIPS 2011 papers on Bayesian nonparametric image segmentation and topic modeling.
September 2011 I'm teaching a fall graduate course on Applied Bayesian Nonparametrics.
July 2011 Extended articles on the HDP-HMM and the HDP-SLDS.
November 2010 Publication of a co-edited Signal Processing Magazine issue on Recent Advances & Emerging Developments of Graphical Models.
October 2010 Weiss and Pearl introduce our review article on Nonparametric Belief Propagation for CACM.
January 2010 I'm teaching a spring graduate course on Learning & Inference in Probabilistic Graphical Models.
August 2009 Publication of a co-edited PAMI special section on Probabilistic Graphical Models in Computer Vision.
May 2008 Release of the TDP Matlab toolbox for part-based, nonparametric modeling of objects and scenes. See our IJCV paper.
Erik B. Sudderth
Assistant Professor
Department of Computer Science
Brown University
I am an Assistant Professor of Computer Science at Brown University. 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 such problems. My more abstract statistical research is inspired by the practical challenges of learning from large, richly structured datasets.
In June of 2006, I completed my Ph.D. in the EECS department at MIT, where I was advised by Professors Alan Willsky and William Freeman. The background chapter of my thesis provides a tutorial introduction to statistical machine learning, including probabilistic graphical models; Monte Carlo and variational inference algorithms such as belief propagation; and nonparametric Bayesian methods based on the Dirichlet process.
Brown University
Brown provides an exciting, interdisciplinary environment for research in statistical machine learning and computer vision:
- Brown Center for Vision Research and Institute for Brain Science
- Brown Machine Learning Reading Group
- Brown Computer Vision Reading Group
- Brown Pattern Theory Group and seminar series
- Applied machine learning: Robotics, Natural Language Processing, Graphics
Contact Information
Tel: (401) 863-7660
Fax: (401) 863-7657
- Directions to my office: CIT Room 509
- Brown University campus map
- Mailing address:
Department of Computer Science
115 Waterman Street
Brown University, Box 1910
Providence, RI 02912
