Michael C. (Mike) Hughes works on statistical machine learning. He develops methods that find useful structure in large, messy datasets and help people make decisions in the face of uncertainty. His research interests include Bayesian hierarchical models, optimization algorithms for approximate inference, model fairness and interpretability, and applications in medicine and the sciences. Active projects include helping clinicians understand and treat diseases like depression and infertility by training probabilistic models to make personalized drug recommendations for new patients based on the thousands of electronic health records observed from previous patients. Hughes completed a Ph.D. in computer science at Brown University in 2016 and spent two years as a postdoctoral fellow at the School of Engineering and Applied Sciences at Harvard University. His research papers and open-source code are available at www.michaelchughes.com