Michael C. Hughes

Michael C. Hughes is an expert in statistical machine learning, with a particular focus on its applications in healthcare. As an Assistant Professor of Computer Science at Tufts University, he delves into the intricacies of optimization algorithms, semi-supervised learning, and model fairness. His work often involves the development and application of Bayesian hierarchical models to analyze diverse data types, aiming to enhance the interpretability and reliability of machine learning models in practical settings. Before joining Tufts University, Michael completed a postdoctoral fellowship at Harvard University, where he honed his expertise in probabilistic models and time-series analysis. He earned his Ph.D. in Computer Science from Brown University, where his research laid the groundwork for his current interests in variational methods and Bayesian inference. His academic journey has been marked by a commitment to advancing the field of machine learning through innovative research and collaboration. Michael's research is driven by a passion for understanding and improving the fairness and efficiency of machine learning models. He is particularly interested in the challenges of semi-supervised learning, where limited labeled data must be leveraged effectively to build robust models. His work in optimization algorithms seeks to enhance the performance and scalability of these models, ensuring they can be applied to real-world problems with confidence. In addition to his research, Michael is dedicated to teaching and mentoring the next generation of computer scientists. He actively engages with students, encouraging them to explore the frontiers of machine learning and its applications. Through his work, he aims to contribute to a more equitable and effective use of technology in society, particularly in the realm of healthcare, where his research has the potential to make a significant impact.