Summary
Michael Burkhart is a senior data scientist with nine years of experience applying advanced machine learning to healthcare and consumer problems, currently developing ML solutions in the Beaulieu-Jones Lab at the University of Chicago. He holds a PhD in Applied Mathematics from Brown and has blended academic rigor with industry impact through roles at Cambridge and Adobe, where he built causal and personalized models at scale. His research has spanned neural decoding and brain–computer interfaces, graph neural networks for predicting brain age, and automating covariate shift detection—demonstrating a knack for translating statistical theory into robust, production-ready pipelines. Comfortable across PyTorch Geometric, PySpark, LightGBM and causal forests, he pairs probabilistic modeling with practical data engineering to tackle noisy, longitudinal biomedical datasets. Based in Chicago, Michael is equally at home mentoring interns on representation learning as he is prototyping novel Bayesian and graph-based approaches for early disease diagnosis.
10 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 Brown University
B.Sc., Statistics, B.Sc., Statistics at Purdue University
M.Sc., Mathematics, M.Sc., Mathematics at Rutgers University