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
- Member of advisory committee and panel for the upcoming NIPS 2015 Workshop on Bayesian Nonparametrics: The Next Generation. Please join us and consider submitting and abstract!
- Area chair for ICML 2015, CVPR 2015, & ICCV 2015.
- Editor, IEEE PAMI Special Issue on Bayesian Nonparametrics, Feb. 2015. (editorial)
- Organizer, ICERM Workshop & Tutorials on Bayesian Nonparametrics, Sept. 2012. (group photo)
- Editor, IEEE Signal Processing Magazine special issue on Recent Advances & Emerging Developments of Graphical Models, Nov. 2010. (editorial)
- Editor, IEEE PAMI Special Issue on Probabilistic Graphical Models in Computer Vision, Oct. 2009. (editorial)