CSE 436
Areas of interest: Large scale computational methods for statistics, machine learning, signal processing

Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in both the Computer Science & Engineering and Statistics departments at the University of Washington. He completed his Ph.D. at the Gatsby Computational Neuroscience Unit at University College London, advised by Peter Dayan, and earned his B.S. in physics at Caltech. Before joining the University of Washington, Sham was a principal research scientist at Microsoft Research, New England. Prior to this, Sham was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania (2010-2012) and an assistant professor at the Toyota Technological Institute at Chicago (2005-2009). Sham completed a postdoc in the Computer and Information Science Department at the University of Pennsylvania under the supervision of Michael Kearns.

He works in the area broadly construed as data science, focusing on designing (and implementing) both statistically and computationally efficient algorithms for machine learning, statistics, and artificial intelligence. His intent is to see these tools advance the state of the art on core scientific and technological problems.

One line of his work has been in providing computationally efficient algorithms for statistical estimation, which has included the estimation of various statistical models with hidden (or latent) structure (including mixture models, topic models, hidden markov models, and models communities in social networks). More broadly, Sham has made various contributions in various areas including statistics, optimization, probability theory, machine learning, algorithmic game theory and economics, and computational neuroscience. He has had numerous roles in chairing conferences and workshops, has given numerous plenary talks, and has received various awards.

For more information, visit Sham's website.