Our Scientific Advisors
MACHINE LEARNING RESEARCHER, PROFESSOR AT MIT
Tamara Broderick is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT where her research focuses on developing and analyzing models for scalable Bayesian machine learning. Among her many awards are a Google Faculty Research Award, the ISBA Lifetime Members Junior Researcher Award, and the Savage Award. She also serves on the Board of Directors of WiML, an organization supporting women in machine learning and AI since 2006.
MACHINE LEARNING RESEARCHER, POSTDOCTORAL FELLOW AT HARVARD
Michael C. Hughes
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
VISION RESEARCHER, PROFESSOR AT UC IRVINE
A leading vision researcher who was once named as one of the top 10 AI researchers to watch out for by the IEEE, heads our Scientific Advisory Board. He is currently a professor of computer science at UC Irvine, formerly at Brown. He is a highly respected researcher in the field of computer vision, and has made significant contributions to the field of scalable machine learning and computer vision.