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His work has been recognized through prize paper awards in Machine Learning, Operations Research and Computer Science, as well as career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society and 2008 ACM Sigmetrics Rising Star Award.

He is a distinguished young alumni of his alma mater IIT Bombay.

Devavrat Shah is a Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology.

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We are interested in reducing the aggregate delay experienced by the master.We focus on linear computations as an essential operation in many iterative algorithms.Much of modern data is generated by humans and drives decisions made in a variety of settings, such as recommendations for online portals, demand prediction in retail, matching buyers and sellers on social platforms, or denoising crowdsourced labels.Due to the complexities of human behavior, the precise data model is often unknown, creating a need for flexible models with minimal assumptions.We pay particular attention to Newton-Sketch and subsampled Newton methods, as well as techniques for solving the Newton equations approximately.

Our tests are performed on a collection of optimization problems arising in machine learning.I'll also say something about the ultimate physical limits of computation, and about speculative proposals for going beyond even quantum computers. Bruton Centennial Professor of Computer Science at the University of Texas at Austin.He received his bachelor's from Cornell University and his Ph D from UC Berkeley, and did postdoctoral fellowships at the Institute for Advanced Study as well as the University of Waterloo.This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during its learning procedure.We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret.A minimal property that is natural for many datasets is "exchangeability", i.e.