Erik B. Sudderth is Professor of Computer Science and Statistics, and Chancellor’s Fellow, at the University of California, Irvine. He directs the UC Irvine Center for Machine Learning and Intelligent Systems, as well as the HPI Research Center in Machine Learning and Data Science at UC Irvine. His research interests include probabilistic graphical models and probabilistic programming, nonparametric Bayesian methods for weakly supervised learning, and applications of statistical machine learning in computer vision and the sciences. Erik was previously an Associate Professor of Computer Science at Brown University, and a postdoctoral scholar at the University of California, Berkeley. He received the Bachelor’s degree (summa cum laude, 1999) in Electrical Engineering from the University of California, San Diego, and the Master’s degree (2002) and Ph.D. degree (2006) in EECS from the Massachusetts Institute of Technology. He received an NSF CAREER award, the ISBA Mitchell Prize, and was named one of “AI’s 10 to Watch” by IEEE Intelligent Systems Magazine.
Ph.D., EECS, MIT, 2006
M.S., EECS, MIT, 2002
B.S., Electrical Engineering, UCSD, 1999
AI, ML and Natural Language Processing
Producing machines to automate tasks requiring intelligent behavior...
Computer Graphics and Vision
Generating, capturing, representing, rendering and interacting with synthetic and real-world images and video...
Statistics and Statistical Theory
Developing and studying methods for collecting, analyzing, interpreting and presenting empirical data...
A probabilistic framework that integrates prior knowledge with new data to update and refine probability …