Using ML for optimizing computation @Ufora

Jul 16, 2015 · New York, United States of America

Talk abstract:
One of the problems that we deal with "under the hood" at Ufora is figuring out how to lay out data across the machines in a cluster for a given computation. So for example, when the user says "do a linear regression on this 100GB dataset", we need to figure out how to automatically distribute and lay out that data across the machines in the cluster in order to minimize travel over the wire. Running a GBM against the same dataset might require a completely different layout of that data. We approach this problem with a number of pretty interesting machine learning techniques (graph theory, PCA, statistical inferences, etc), and happen to think it's a super interesting problem!

Speaker - Braxton McKee
Braxton is the technical lead and founder of Ufora, a software company that has built an adaptively distributed, implicitly parallel runtime. Before founding Ufora with backing from Two Sigma Ventures and others, Braxton led the ten-person MBS/ABS Credit Modeling team at Ellington Management Group, a multi-billion dollar mortgage hedge fund. He holds a BS (Mathematics), MS (Mathematics), and M.B.A. from Yale University.

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