Erik B. Sudderth, Statistical Computation & Perception
I am a Professor of Computer Science and Statistics, and Chancellor's Fellow, at the University of California, Irvine. My Learning, Inference, & Vision Group develops statistical methods for scalable machine learning, with applications in artificial intelligence, computer vision, and the natural and social sciences. My research affiliations at UC Irvine include:
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, advised by Professors Michael Jordan and Stuart Russell, focused on Bayesian nonparametric models (see my CVPR tutorial).
For more information: bio · curriculum vitæ · research projects & code · publications & lectures
- Action editor for the Journal of Machine Learning Research.
- Associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence.
- Scientific committee for the 12th International Conference on Bayesian Nonparametrics.
- Advisory committee for the 2018 NeurIPS Workshop on All of Bayesian Nonparametrics.
- Sponsor chair for the 2018 & 2019 International Conference on Machine Learning.
- Area chair for
NeurIPS 2021 & 2019 & 2016,
CVPR 2019 & 2015,
ICML 2017 & 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)