Machine Learning

New machine learning hires for UW Computer Science & Engineering and Statistics

Sham Kakade and Sergey Levine will join the University of Washington this fall, further strengthening UW’s excellent machine learning group.

Sham joins as the holder of a Washington Research Foundation Data Science Chair appointed jointly in Computer Science & Engineering and Statistics. His research has ranged from economics to neuroscience to applied and theoretical machine learning and their intersection. He has made significant contributions to semi-supervised learning, online learning, reinforcement learning, and learning of latent-variable and hidden Markov models. Sham is currently Principal Research Scientist at Microsoft Research, New England. Prior to Microsoft Research, he was Associate Professor in the Wharton Statistics Department at University of Pennsylvania and Assistant Professor at Toyota Technological Institute, Chicago. Sham received his Ph.D. at the University College London Gatsby Computational Neuroscience Unit and his B.S. in Physics at Caltech.

Sergey works at the intersection of robotics, machine learning, graphics, and animation. He pioneered the recent trend in using deep learning to create neural network controllers for animated characters and robots. His learning techniques enable robots to solve control tasks that have been elusive using traditional approaches. This past week he won the Best Robotic Manipulation Paper Award at ICRA, the IEEE flagship robotics conference, for his work on learning controllers for complex manipulation tasks. Sergey received his Ph.D. in 2014 from Stanford University and joins the Computer Science & Engineering faculty following a postdoc with Pieter Abbeel at UC Berkeley.

Sham and Sergey join UW’s outstanding machine learning faculty including Carlos Guestrin, Pedro Domingos, Emily Fox, Daniela Witten, Marina Meila, Jeff Bilmes, Mathias Drton, Maryam Fazel, Noah Simon, and Thomas Richardson.

UW is one of the world's top centers of research in machine learning. We are active in most major areas of ML and in a variety of applications like natural language processing, vision, computational biology, the Web, and social networks. Check out the links on the left to find out who's who and what's happening in ML at UW.

And be sure to see our CSE-wide efforts in Big Data


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