CD3 Seminar

Abstract: The goal of this work is to computationally rediscover the Maxwell Equations based on data from virtual experiments. As a step in this direction, recent work has focused on rediscovering a general force law composed of Newton¹s Universal Law of Gravitation, Coulomb¹s Law, and the force generated by a magnetic dipole. The basic approach is essentially a combinatorial optimization over a language that adapts as new partial theories are discovered: essentially finding linguistic patterns in theories. Some promising initial results are presented, and future plans sketched. Short Bio: Dr. Mark Stalzer is a Principal Computational Scientist in the Center for Data-Driven Discovery at the California Institute of Technology. He led two high-profile research labs in computing/information sciences at Hughes (1999-2004) and then Caltech (2005-2013). Mark was the Fellow of Data Science at the Moore Foundation (2013-2015) where he was a senior advisor to their Data-Driven Discovery initiative. He is trained in physics and computer science, receiving his PhD from USC in 1993. Mark is an ACM Distinguished Scientist and an AAAS Fellow.