Erik Sudderth

Director of the Center for Machine Learning
Associate Professor, Computer Science and Statistics

Erik previously spent seven great years on the faculty at Brown University, where he remains an Adjunct Associate Professor of Computer Science. His Learning, Inference, & Vision Group develops statistical methods for scalable machine learning, with applications in artificial intelligence, vision, and the natural and social sciences. His 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 his current role as Director of the Center for Machine Learning, he works with ICS’s growing bench of over a dozen researchers in the fields of machine learning and intelligent systems to addresses the myriad ways we can develop computer algorithms to harness the vast amounts of digital data available in the 21st century. Erik plays a critical role in ICS’s vision by bringing together the interdisciplinary expertise across the campus and community to develop data-based solutions to address globally unprecedented issues of our increasingly digital age.

Visit Erik’s full bio for additional background.

Sameer Singh

Assistant Professor of Computer Science

Sameer is working on robustness and interpretability of machine learning algorithms, along with models that reason with text and structure for natural language processing. Sameer was a postdoctoral researcher at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs. His group has received funding from Allen Institute for AI, NSF, DARPA, Adobe Research, and FICO, and published extensively at top-tier machine learning and NLP venues.