Erik B. Sudderth, Statistical Computation @ Brown University
I am an Associate 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
- Associate editor for IEEE Transactions on Pattern Analysis and Machine Intelligence.
- Member of advisory committee and panel for the recent NIPS 2015 Workshop on Bayesian Nonparametrics: The Next Generation. Thanks for your contributions!
- Area chair for ICML 2015, CVPR 2015, ICCV 2015, & NIPS 2016.
- 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)