Michael Catchen is a computational ecologist and postdoctoral researcher with a decade of experience combining machine learning, computational statistics, and field-driven ecology to improve biodiversity monitoring and forecasting. Currently an IVADO Postdoctoral Fellow at Université de Montréal, he adapts modern computer-vision and gradient-boosting techniques to advance species distribution modeling and optimal biodiversity-observation network design. His PhD from McGill focused on using simulation and ML to predict ecological dynamics and species interaction networks, and he has hands-on experience building geospatial analysis tools in a GEO BON–Microsoft partnership. Michael’s background spans research software development, teaching, and embedded systems work at JPL, reflecting an unusual mix of field ecology, production-grade tooling, and low-level programming. Colleagues will note his wry Github bio—“not actually a frog”—hinting at a playful curiosity that complements rigorous, reproducible science.
10 years of coding experience
5 years of employment as a software developer
Master of Arts - MA, Ecology and Evolutionary Biology, Master of Arts - MA, Ecology and Evolutionary Biology at University of Colorado Boulder
Doctor of Philosophy - PhD, Ecology, Doctor of Philosophy - PhD, Ecology at McGill University
population and community dynamics on spatial graphs, in julia.
Contributions:50 commits, 2 PRs, 219 pushes in 1 year 2 months
juliaecojuliadynamicssimulationecology
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