Some time ago there was a question about the relevance of computer science in practical engineering situations. The same discussion came up in a group on LinkedIn and there was one input that I would like to share with you with the permission of the original author, Anupam Gupta,Research Lead at Mu Sigma: * Michael Kearns: Prof. Kearns is a theorist. Yes! An AI and ML theorist with some very important results on complexity of ML/AI algorithms. Where did he put his theoretical background to use? Look at the Penn-Lehman automated trading project . * Late Prof. Rajeev Motwani had a lot of significant work on Data Streams that has been put to very good use in Industry. theory.stanford.edu/~rajeev/ * Late Prof. Partha Niyogi has put Differential Geometry (Geometric Methods and Manifold Learning) to practical ML/AI problems. * A recent example is Prof. Gunnar Carlsoon who along with his past and current students is changing the paradigm of how we analyse data using highly abstract ideas from Algebraic Topology. * Compressed Sensing that has become almost a standard in efficiently acquiring and reconstructing a signal has come from pure mathematicians -- one of them, Terence Tao, a Fields Medalist for his research in Mathematical Analysis. * Look at Googles Algorithms and Theory page to get a better sense of how to put pure ideas to use: research.google/pubs/AlgorithmsandTheory.html * Look at MSR Theory page to get more perspective: research.microsoft/en-us/groups/theory/
Posted on: Sat, 08 Mar 2014 06:27:42 +0000
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