Michael Hughes
Research/Areas of Interest
Machine learning : probabilistic models, Bayesian inference, variational methods, time-series analysis, semi-supervised learning
Clinical informatics : electronic health record analysis
Education
- 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
Biography
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 automatically diagnose cardiovascular disease from ultrasound images of the heart and predicting risk of mortality from the time-varying vital signs and laboratory results available in electronic health records.
Visit his website -- www.michaelchughes.com -- for links to recent papers, open-access datasets, and open-source code.
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.
Visit his website -- www.michaelchughes.com -- for links to recent papers, open-access datasets, and open-source code.
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.