When not climbing in the Alps, racing a carbon fiber Cervélo, or mountain biking through some jungle, I am a lecturer in Mathematics & Computer Science in the Odyssey Program at Johns Hopkins University, and a Specialist in Computational Geography & Geographic Information Science (GIS) at the Library of Congress.

Over the past few years I have lectured or taught classes on Quantum Computing, Evolutionary Game Theory, the Mathematical Foundations of Deep Learning, Stochastic Processes & Markov Chains and most recently, on the Geometry of Voting Systems.

Co-Chair of the ALA Artificial Intelligence & Machine Learning Research Group, I often lecture on the use of Deep Learning in library and museum environments.

A Fellow of the Royal Geographical Society, I am the founder of the λ-LAB where our philosophy is to apply mathematical modeling, machine learning, and geospatial data, to study difficult policy issues facing the world today. Our current projects center on studying the geometry of voting systems, the mathematics of political polling, and the complexity of the redistricting problem.

The lab's theoretical research is focused on the use of the renormalization group to study the function of deep neural networks. At the λ-LAB, we often find ourselves pondering the conceptual intricacies of theoretical linguistics, wondering about quantum category theory and marveling at the philosophical complexity of genetic algorithms.

The author of more than one hundred articles and books, including the New York Times bestseller, MAP: Exploring the World, my writing and work has been featured in many media outlets including the New York Times, Washington Post, Discover Magazine, CBS News and on NPR’s All Things Considered.

Currently, I am trying to turn a mess of scribbled mathematical notes into a book called, Lectures on Mereotopology and the Logical Foundations of GIS.

I find being close to the gentle hum of supercomputers, the silence of a remote alpine peak, and the everyday language of James Joyce’s Ulysses, strangely comforting.