معرفی
Michael Hughes is an Assistant Professor of Computer Science at Tufts University's School of Engineering. His research focuses on statistical machine learning and its applications in healthcare, including probabilistic models, Bayesian inference, and semi-supervised learning. He leads projects such as automated cardiovascular disease diagnosis via echocardiograms and mortality risk prediction from electronic health records.
Education:
- PhD, Computer Science, Brown University (2016)
- MS, Computer Science, Brown University (2012)
- BS, Computer Science, Franklin W. Olin College of Engineering (2010)
Research Interests: Bayesian hierarchical models, variational inference, clinical informatics, and interpretable AI. His work bridges theoretical advancements with high-impact applications in healthcare.
Awards & Activities:
- Recipient of multiple reviewer awards at top conferences (NeurIPS, AISTATS)
- Organizer of workshops on Bayesian methods and health informatics
- Principal investigator on grants from NSF, NIH, and the US Army
Teaching: Courses include Intro to Machine Learning, Bayesian Deep Learning, and Statistical Pattern Recognition.
Labs & Teams: Leads the Tufts Machine Learning research group and collaborates with clinicians at Tufts Medical Center on healthcare AI projects.
