Highlights

May 2014 An exciting ICML 2014 paper on robust MAP estimation via diverse particle max-product.

March 2014 I'm thrilled to receive an NSF CAREER Award for my work on large-scale Bayesian nonparametric learning.

November 2013 NIPS papers on scalable variational methods for nonparametric clustering and relational modeling.

September 2013 I'm teaching a fall Introduction to Machine Learning.

April 2013 BSSA article on our NET-VISA system for global seismic monitoring, based on data from the CTBTO.

January 2013 I'm teaching a spring graduate course on Probabilistic Graphical Models.

November 2012 NIPS papers seeking stable, effective, & scalable Bayesian learning. Plus, understanding 3D bodies in motion.

September 2012 Thanks to everyone who attended our ICERM Workshop and Tutorials on Bayesian Nonparametrics.

June 2012 Join me for my CVPR tutorial on Applied Bayesian Nonparametrics.

October 2010 Weiss and Pearl introduce our review article on Nonparametric Belief Propagation for CACM.

 
Erik Sudderth

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 (see my CVPR tutorial) 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.

I am currently co-editing an IEEE PAMI Special Issue on Bayesian Nonparametrics. In the past, I have co-edited an IEEE Signal Processing Magazine special issue on Recent Advances & Emerging Developments of Graphical Models, and an IEEE PAMI Special Section on Probabilistic Graphical Models in Computer Vision. I was happy to co-organize a 2012 ICERM Workshop and Tutorials on Bayesian Nonparametrics.

Brown University

Brown provides an exciting, interdisciplinary environment for research in statistical machine learning and computer vision:

Contact Information


email address
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