Zachary Kilpatrick is an Associate Professor of Applied Mathematics at the University of Colorado Boulder who studies how complex systems—ranging from neural circuits to social groups—learn, adapt, and make decisions using mathematical modeling, data science, and machine learning. With eight years post-PhD experience in academia and over $2M as PI/co-PI on NSF and NIH awards, he builds theoretical and data-driven models that reveal information flow and adaptation in dynamic systems. He designs hands-on Python labs, auto-graded Jupyter assignments, and executive education curricula, and is authoring a Coursera specialization that bridges computational neuroscience and AI. Zachary consults on data-driven modeling and technical education for organizations like the Allen Institute and Data Society, translating rigorous math into practical, interpretable models for real-world data. He brings uncommon breadth: deep training in dynamical systems and PDEs paired with applied expertise in time-series, statistical inference, and large-language-model applications. Based in Louisville, Colorado, he prioritizes improving model interpretability and creating scalable training that helps teams deploy principled analytics.
8 years of coding experience
8 years of employment as a software developer
Bachelor of Arts - BA Computational and Applied Mathematics, Bachelor of Arts - BA Computational and Applied Mathematics at Rice University
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