When not climbing in the Alps or racing a carbon fiber Pinarello, I am an applied mathematician, geographic information systems scientist and former lecturer at Johns Hopkins University, specializing in the mapping of infectious diseases. I am currently based in Nice, France.

I presently teach occasional seminars in the theoretical foundations of GIS for the mapping of infectious disease, and the mathematics of deep and machine learning for infectious disease modeling at University College London, and at Sorbonne Université - Campus Pierre et Marie Curie, in Paris.

Formerly a specialist in computational geography and GIS at the Library of Congress, and advisor to the US Congress and Senate on the geospatial dynamics of infectious disease transmission, I am the founder and director of BIOMAP AI , where we use bioinformatic and genomic data, machine learning, and advanced geographic information systems, to study the far-from-equilibrium and non-stationary transmission and risk of zoonotic diseases, especially the movement patterns of their animal hosts.

BIOMAP AI works and partners with government and NGO based agencies, like the World Health Organization's GIS Center for Health, researching complex problems in spatial epidemiology, accessing risk of disease outbreaks, and modeling geographic uncertainty, to help save lives and reduce economic impacts.

Our current projects are centered on using wavelet and non-linear approximation methods to study the SARS-CoV-2 pandemic , and other past epidemics, like the medieval patterns of plague transmission in Europe and Eurasia , and on reconstructing the dynamics of the 2014-2016 Ebola epidemic in west Africa .

We are also currently working to map and study the movement patterns of Bundibugyo virus outbreak in the Democratic Republic of the Congo, the measles cases in the United States, and on reconstructing the primary sources and statistics used by Daniel Defoe in his Journal of the Plague Year.

BIOMAP AI’s theoretical work concentrates on the application and development of signal processing techniques, especially wavelets , and machine learning to high-dimensional epidemiological geospatial time-series data, and on the development of Levi and fluid flow style models to zoonotic disease host movement ecology .

My mathematical research concentrates on developing solutions to the uncertain geographic context problem, on deriving an algebraic basis for the change-of-support issue in spatial-temporal data, and formalizing the underlying mathematics of the modified areal / temporal unit problems-- all problems critical to accurate disease mapping.

Convinced that point-free geometries and contact algebras can be used as a deeper ontological and conceptual foundation for a more process, event based, and dynamic GIS, I am exploring the use of the formal structures of dynamic mereotopology and region-based topology.

The author or editor of more than one hundred articles and books, including the New York Times bestseller and NPR selection, 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 .

His latest article, To Save Lives: Lessons of a Pandemic Cartographer, was recently published in the Transactions of the Institute of British Geographers.

A Fellow of the Royal Geographic Society, I finds being close to the gentle hum of supercomputers, pondering the complexities of ancient DNA, and exploring the deep labyrinths of the renormalization group, strangely comforting.