Erik B. Sudderth, Statistical Computation & Perception

I am an Associate Professor of Computer Science and Statistics at the University of California, Irvine. I previously spent seven great years on the faculty at Brown University, where I remain an Adjunct Associate Professor of Computer Science. 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

Research Highlights

• Our work on 3D scene reconstruction with multi-layer depth and epipolar transformers appears at ICCV 2019. Previously at the CVPR 3D Scene Understanding and SUMO Challenge workshops.
• A Facebook Probability and Programming research award supports work on scalable variational inference for probabilistic programs.
• Our cascaded 3D detection framework, which integrates geometric and contextual cues for robust scene understanding from RGB-D images, is summarized by a 2019 paper appearing in IEEE PAMI.
• An NSF Robust Intelligence Award with Alex Ihler supports work on new particle-based algorithms for inference and learning with continuous graphical models. I gave a talk at the 2017 SoCal Machine Learning Symposium about our earlier diverse particle max-product algorithm, which gives state-of-the-art predictions of continuous protein side-chain conformations. Code available.
• At AISTATS 2018, our framework for prediction-constrained training of probabilistic models leads to improved semi-supervised learning of topic models, with applications to the analysis of documents and electronic health records. This work received the SoCal NLP Symposium best paper award.
• An NSF CAREER Award supports our open source toolbox BNPy: Bayesian Nonparametric clustering for Python. BNPy implements scalable, stochastic and memoized variational inference algorithms for a diverse range of Bayesian nonparametric models.
• Work with BrainGate on multiscale semi-Markov dynamics for improved brain-computer interfaces appeared at NIPS 2017. A supplemental video demonstrates accurate, interactive control of a computer cursor by a clinical trial participant with tetraplegia.
• The 2014 ISBA Mitchell Prize for Bayesian analysis of an important applied problem goes to our NET-VISA system for global seismic monitoring, learned from data provided by the comprehensive nuclear-test-ban treaty organization (CTBTO). For details see the Brown University news article.
• Weiss & Pearl introduce our review article on Nonparametric Belief Propagation for the CACM.

Editorial Highlights

• 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 2019 & 2016, CVPR 2019 & 2015, ICML 2017 & 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)

Erik B. Sudderth
E: lastname@uci.edu
P: (949) 824-8169

Office: Donald Bren Hall 4028
Mailing Address:
University of California, Irvine
School of Information & Computer Sciences
Irvine, CA 92697-3435