Hector Zenil, Ph.D., Research Fellow, University of Oxford

Techniques from graph theory, statistics and entropy have been adapted and often successfully used in different disciplines for characterizing complex data in the language of networks. Zenil will explain how a novel algorithmic calculus can be exploited to reprogram systems, in particular genetic regulatory networks, showing that Shannon’s entropy, Kolmogorov-Chaitin’s complexity and Solomonoff-Levin’s algorithmic probability can quantify different properties of static and evolving (labeled and unlabeled) graphs.

This Coffee@Beyond event is presented by the Beyond Center for Fundamental Concepts in Science. For more information, visit beyond.asu.edu.

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