John W. Hessler
GIS Scientist, Engineer, and Professor in Baltimore, MD
When not climbing in the Alps, sitting in the saddle of a Cervelo, or mountain biking through some jungle, I am a Specialist in Computational Geography & Geographic Information Science (GIS) at the Library of Congress in Washington DC, and a lecturer in Evolutionary & Quantum Computing in the Graduate School of Advanced Studies of the Krieger School of the Arts and Sciences at the Johns Hopkins University.
An avid climber and mountaineer, I am a frequent contributor to Alpinist Magazine, where I write on cartography, the history of climbing, high-altitude physiology & the effects climate change on the sport of mountaineering.
Over the past few years I have lectured on or taught seminars about the Navier-Stokes equations, mathematical theories of deep learning, evolutionary computing & racing bike design, quantum field theory & computing, and the mathematical & algorithmic foundations of GIS.
My current academic & theoretical research focuses on the mathematics and conceptual foundations of Deep Learning and on the use of the renormalization group, derived from quantum field theory, to study the complexity of artificial neural networks. Perplexed by the efficiency of stochastic gradient descent and back-propagation, I am studying the shape and geometry of high dimensional error landscapes and surfaces.
The author of more than one hundred articles and books, including the New York Times best-seller, MAP: Exploring the World, my writing and 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.
Interested in the applications of Clifford Algebras and in the logical formalization of the foundations of GIS, I am currently working on a book entitled, Spatial Algebras: the Topological and Mereological Foundations of Geographic Information Science.
Founder and principle engineer at the Flow Lab for Racing Bicycle Design, where we apply methods from computational fluid dynamics and evolutionary computing to the design optimization and scientific study of racing bicycles, I find being close to the gentle hum of supercomputers, pondering the subtle, yet meaningful, complexities of Cervelo carbon fiber frames, and dreaming of a faster ascent up to the sublime heights of the Col du Galibier, strangely comforting.