Erik came to Brown CS in 2009 after receiving his B.S. from the University of California, San Diego and his Sc.M. and Ph.D. from MIT, and serving as a postdoctoral scholar at the University of California at Berkeley. In 2004-05, he was the recipient of an Intel Foundation Doctoral Fellowship, and in 2008 he was named one of “Ten to Watch” in Artificial Intelligence by IEEE Intelligent Systems Magazine.
Erik leads the Learning, Inference, and Vision Group, which 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)
In 2014, Sudderth received an NSF CAREER Award, and his recent work has been used by geologists to help predict landslides and to advance seismic monitoring and nuclear non-proliferation, earning him the Mitchell Prize, one of the most prestigious awards in Bayesian analysis.