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If you have thoughts on how to do this, feel free to contact me. Quixotic though it may sound, I hope to use computer science and statistics to change the world for the better. Join Facebook to connect with Dwivedi Raaz and others you may know. Lately, I've been developing and analyzing scalable learning algorithms for healthcare, climate forecasting,Īpproximate posterior inference, high-energy physics, recommender systems, 0 Thesis A Dwivedi, Raaz T Principled Statistical Approaches For Sampling and Inference in High Dimensions I EECS Department, University of California, Berkeley. View the profiles of people named Dwivedi Raaz. I got my first taste of research at the Research Science Institute and learned to think deeply of simple things at the Ross Program. His research interests center around developing a theoretical understanding of algorithms used in statistics, optimization and machine learning. advisor was Mike Jordan, and my undergraduate research advisors were Maria Klawe and David Walker. Raaz Dwivedi is a fourth-year graduate student in the Department of EECS at the University of California, Berkeley where he is jointly advised by BMW-Y: Martin Wainwright and Bin Yu. Math+X postdoctoral fellow, working with Emmanuel in Computer Science (2007) from Princeton University.īefore joining Microsoft, I spent three wonderful years as an assistant professor of Statistics and, by courtesy, Computer Science at Stanford and one as a Simons in Statistics (2011) from UC Berkeley and my B.S.E. I'm a statistical machine learning researcher at Microsoft Research New England and an adjunct professor at Stanford University.