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John Hessler

When not searching through ruins in Central America, climbing in the Alps or mountain biking through some jungle, John W. Hessler is a Specialist in Computational Geography and Geographic Information Science at the Library of Congress in Washington, DC and a Lecturer in the Graduate School of Advanced Studies of the Krieger School of Arts and Sciences at Johns Hopkins University in Baltimore, MD. The founder of the Topology Lab for the Foundations of Quantum Computing he has taught seminars in quantum field theory and computing, the Navier-Stokes equations, the mathematics of deep learning, quantum information theory and supercomputing GIS. His current research focuses on the use of renormalization group and random matrix methods to study the mysterious mathematics and computational function of deep learning algorithms and networks. Interested in the cognitive science of spatial neural encoding, Hessler is also studying the complex mathematics of grid and place cells and their relationship to the cognitive map. He is also interested in the theory of Markov Chains and their application to mapping, redistricting and gerrymandering problems, and in the application of supercomputing GIS to the study of voting systems around the world. The author of more than one hundred books and articles, including the New York Times bestseller, MAP: Exploring the World, his work has been featured in many national media outlets including the New York Times, Washington Post, Discover Magazine, WIRED, the Atlantic’s CITYLAB, the BBC, CBS News and most recently on NPR’s All Things Considered. An avid mountaineer, he is currently writing about the Navier-Stokes Equation and glacial flows. Hessler is also an occasional contributor to Alpinist Magazine, he is finishing up a book entitled Collecting for a New World: treasures of the early Americas to be published in Fall 2019.

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