Research/Areas of Interest:
Machine learning, probabilistic models, optimization, clinical informatics, data science.
PhD, Computer Science, Brown University, USA, 2016
MS, Computer Science, Brown University, USA, 2012
BS, Computer Science, Franklin W. Olin College of Engineering, USA, 2010
Michael C. Hughes ("Mike") is an Assistant Professor of Computer Science at Tufts University, where he does research in statistical machine learning and its applications to healthcare. His goal is to develop predictive and explanatory models 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 for documents, sequences, networks, and images; optimization algorithms for approximate inference; model fairness and interpretability; and semi-supervised learning. Active projects include helping clinicians understand and treat depression or infertility by training personalized drug recommendation models based on thousands of electronic health records observed from previous patients. You can find his papers and open-source code on the web at www.michaelchughes.com.
Previously, from 2016-2018 he was a postdoctoral fellow in computer science at Harvard's School of Engineering and Applied Sciences (SEAS), advised by Prof. Finale Doshi-Velez. He completed a Ph.D. in the Department of Computer Science at Brown University in 2016, advised by Prof. Erik Sudderth.