John Hessler, FRGS

Applied Mathematician, Geographic Information Systems Scientist, and Professor in Baltimore, MD.

John Hessler, FRGS

Applied Mathematician, Geographic Information Systems Scientist, and Professor in Baltimore, MD.

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When not climbing in the Alps or racing a carbon fiber Cervélo, I am an applied mathematician, Geographic Information Systems (GIS) scientist, and lecturer at Johns Hopkins University, specializing in spatial epidemiology, and on modeling the transmission of zoonotic diseases and the movement of their animal hosts.

I am the director of the biomap-lab, where our present research centers on mapping the complex transmission pathways of the 2014-2016 Ebola virus outbreak in West Africa, modeling the early spatial dynamics of SARS-CoV-2, and tracing the movement of avian H5N1 influenza in wild bird & mammal populations.

An affiliated researcher at the Data Science & AI Institute at Johns Hopkins, I am exploring the application of machine and deep learning techniques in GIS environments and have recently joined the struggle to understand the mathematics of deep neural networks, using renormalization group methods.

My theoretical work concentrates on the use of wavelet analysis and spectral methods in spatial epidemiology, on the development of Markov Random Field disease mapping algorithms, and on computational approaches to the uncertain geographic context problem.

Formerly a specialist in computational geography and geographic information science at the Library of Congress, I have provided geospatial policy analysis to members of Congress, staff and Congressional committees on a wide range of computationally intractable policy issues, like redistricting, the dynamics of SARS-CoV-2, and the use differential privacy algorithms by the US Census.

Over the last decade at Johns Hopkins University, I have given lectures or taught the seminars, Bioinformatics and the Mapping of Disease; Bayesian Disease Mapping; Evolutionary Game Theory; Fourier and Time Series Analysis in Spatial Epidemiology; the History and Archaeology of Pandemics; and Machine Learning for GIS.

The author or editor of more than one hundred articles and books, including the New York Times bestseller, MAP: Exploring the World, I am now trying to turn a mess of mathematical course notes into the forthcoming book, Lectures on Mereotopology and the Ontological Foundations of Geographic Information Science.

A Fellow of the Royal Geographical Society, I find being close to the gentle hum of supercomputers, pondering the complex labyrinths of renormalization group theory, and exploring the philosophical knots of evolutionary algorithms, strangely comforting.