Michael C. Hughes

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging.

• PhD, Computer Science, Brown University (2016)
• MS, Computer Science, Brown University (2012)
• BS, Computer Science, Franklin W. Olin College of Engineering (2010)

Research interests center on:

• Bayesian hierarchical models for documents, sequences, and medical images
• Optimization algorithms for approximate inference
• Model fairness and interpretability in clinical contexts
• Semi-supervised learning for medical diagnostics

Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research.

Scientific awards include:

• NIH R01 Award (PI) for heart valve disease detection (2025)
• NSF CAREER Award (2024)
• NSF GCR Grant (2024)
• Best Poster Award at Time Series Workshop (ICML 2021)
• Top 10% Reviewer Awards at AISTATS (2023, 2022)

Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.