Summary
Michael Schmidt is a Principal ML Engineer in Denver with 12 years of experience building mathematically rigorous, production-ready AI systems that prioritize interpretability and uncertainty quantification. He specializes in Bayesian methods—Gaussian processes, change-point detection, and probabilistic novelty detection—and has applied them across agriculture, healthcare, law, and finance to deliver actionable forecasts and decision tools. At Redpoll and Ix he led DARPA-linked research and deployed models for time-series inference and reinforcement learning under uncertainty, while at CiBO delivered a crop-yield forecast with industry-leading accuracy. With an MS in Physics, ongoing PhD work in Applied Mathematics, and a background spanning DevOps, teaching, and high-performance computing, he blends deep theory with pragmatic engineering. Colleagues describe him as a researcher-engineer who turns complex probabilistic ideas into transparent tools for real-world impact.
12 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD Applied Mathematics, Doctor of Philosophy - PhD Applied Mathematics at University of Colorado Denver
MS Physics, MS Physics at University of Colorado Boulder
English, Latin