“Imagining how a cell thinks: The design of reaction network schemes that do machine learning”

CS Colloquium Seminar: Dr. Manoj Gopalkrishnan from IIT Bombay


Host: David Doty

When: Thursday, March 15th at 3:10pm

Where: 1131 Kemper Hall

Abstract: A living cell responds in sophisticated ways to its environment. Such behavior is all the more remarkable when one considers that a cell is a bag of molecules. A detailed algorithmic explanation is required for how a network of chemical reactions can produce sophisticated behavior. Several previous works have shown that reaction networks are computationally universal and can, in principle, implement any algorithm. The problem is that these constructions have not mapped well onto biological reality, have made wasteful use of the computational potential of the native dynamics of reaction networks, and have not made any contact with statistical mechanics. We seek to address these problems.
We find that the mathematical structure of reaction networks is particularly well suited to implementing modern machine learning algorithms. We describe a novel reaction network scheme for solving a large class of statistical problems including the problem of how a cell would infer its environment from receptor-ligand bindings. Specificially we show how reaction networks can implement information projection, and consequently a generalized Expectation-Maximization algorithm, to solve maximum likelihood estimation problems in partially-observed exponential families on categorical data. Our scheme can be thought of as an algorithmic interpretation of E. T. Jaynes’s vision of statistical mechanics as statistical inference.

Bio: Manoj Gopalkrishnan is an Associate Professor in the Department of Electrical Engineering at IIT Bombay. He received his Ph. D. in Computer Science from University of Southern California, and a B. Tech. in Computer Science and Engineering from IIT Kharagpur. In his research, he seeks for algorithmic explanations for scientific phenomena. Specifically, his investigations have led him to explore chemical reaction networks and their information processing possibilities, as well as to connections between thermodynamics and information processing.

1131 Kemper Hall

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