Summary
Philippe Desjardins-proulx is a computational scientist and postdoctoral researcher at Université de Montréal with 14 years of experience bridging rich logic formalisms and probabilistic methods to tackle problems in AI and disease ecology. His Ph.D. work on automatic revision of ecological theories synthesizes first-order and type-theoretic logics with many-valued and probabilistic reasoning, and he now explores integrating higher-order/type theory with Bayesian probabilistic programming (Pyro, Anglican). A seasoned systems coder, he implements high-performance algorithms in C/C++ and Rust (CUDA experience since 2009) while exposing interfaces to Python, and has applied these skills to large-cluster simulations and ecological modeling. He also brings practical ML experience from standard methods to novel algorithms for learning fuzzy rules, plus domain expertise in speciation and food-web theory that informs his probabilistic models. Notably, he combines formal logic rigor with hands-on HPC engineering—a rare mix that helps translate theoretical AI advances into scalable, reproducible scientific software.
14 years of coding experience
Bachelor of Science (B.Sc.), Mathematics and Biology, Bachelor of Science (B.Sc.), Mathematics and Biology at Université du Québec
Postdoctoral Fellowship, Artificial Intelligence, Postdoctoral Fellowship, Artificial Intelligence at Université de Montréal
Graduate certificate, Bioinformatics (specialization in machine learning & statistics), Graduate certificate, Bioinformatics (specialization in machine learning & statistics) at University of Illinois at Chicago
Doctor of Philosophy (Ph.D.), Machine Learning & Ecology, Doctor of Philosophy (Ph.D.), Machine Learning & Ecology at Université de Sherbrooke
Stanford University
French, English