John W. Hessler
Applied Mathematician, Computer Scientist, and Professor in Baltimore, MD.
John W. Hessler
Applied Mathematician, Computer Scientist, and Professor in Baltimore, MD.
When not climbing in the Alps or racing a carbon fiber Cervélo, I am an applied mathematician, computer scientist, and lecturer in the Odyssey Program at Johns Hopkins University where I have given lectures or taught seminars on Quantum Computing & Information; the Mathematical Foundations of Deep Learning; Neural Networks and Renormalization; Category Theory for Computer Science; Algorithmic Foundations of Geographic Information Science; Mereology, Topology and the Foundations of Geographic Information Science; and Bioinformatics and the Mapping of Disease.
In the summer 2023 I am teaching Geospatial Phylogenetics & the Mapping of Disease at the Sorbonne in Paris, and a NLP virtual seminar at JHU called, Inside ChatGPT: exploring the science behind the bot.
I am the founder and the director of the biomap-lab, where we are developing new statistical and computational tools for mapping and visualizing the dynamics of far from equilibrium spatial processes, like the spread of pandemics, non-stationary urban processes, and mass population movement.
Our current computational research centers on retrospectively mapping the spatial phylodynamics and complex transmission pathways of the 2014-2016 Ebola virus outbreak in West Africa, and on studying the geographic distribution of the earliest cases of SARS-CoV-2.
Trying to push the boundaries of disease mapping, our theoretical research is focused on the development of Bayesian disease mapping techniques, and on the application of adaptive Gaussian Markov random fields.
We are also interested in the use of deep learning models in GIS environments, and have joined the struggle to understand the mathematical foundations of artificial neural networks, using renormalization group methods.
The author of more than one hundred articles and books, including the New York Times bestseller, MAP: Exploring the World, I am presently trying to turn a mess of mathematical course notes into the book, Lectures on Mereotopology and the Ontological Foundations of Geographic Information Science.
I find being close to the gentle hum of supercomputers, exploring the twisted labyrinths of quantum category theory, and pondering the philosophical depths of evolutionary algorithms, strangely comforting.