John W. Hessler, FRGS

Mathematician, GIS Scientist, and Professor in Baltimore, MD

John W. Hessler, FRGS

Mathematician, GIS Scientist, and Professor in Baltimore, MD

When not climbing in the Alps or racing a carbon fiber Cervélo, I am the director and founder of the λ-LAB (Lambda-Lab), where our philosophy is to apply high-performance computing, mathematical modeling, and geospatial analysis, to some of the most difficult humanitarian and policy issues facing the world today.

At the λ-LAB we are developing new statistical and computational tools for modeling the dynamics of far from equilibrium spatial processes, like the spread of pandemics and refugee flows, along with more traditional political issues, such as the computational complexity of Congressional redistricting.

Recently, our theoretical work has expanded to include other data driven policy issues, like the mathematics of the database reconstruction theorems and the effects of differential privacy algorithms on the 2020 Census.

Formerly a specialist in Computational Geography and Geographic Information Science at the Library of Congress, I have provided geospatial policy analysis to Congressional members, staff, and committees on wide variety of issues from SARS-CoV-2 and economic inequality, to the Census and population demographics, to health care and internet access.

A lecturer at the Johns Hopkins University, over the last few years I have given lectures about or taught seminars in Social Choice & Game Theory; the Mathematical Modeling Refugee Flows; the Mathematical Structure of Voting Procedures; and the Mathematics of the Redistricting & Gerrymandering Problem. I also teach courses in the theoretical foundations of GIS in the Pierre and Marie Curie Faculty at Sorbonne Université in Paris.

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

Imagining a four-dimensional and process based ontology for the next generation of temporal GIS computing, I am trying to turn a mess of mathematical course notes into the book, Lectures on Mereotopology & 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 depths of Arrow's Impossibility Theorem, and confronting the intricate philosophical knots of evolutionary algorithms, strangely comforting.