Michael D. Porter

Michael Porter is a data scientist, statistician, and engineer whose career spans academia and industry. He has held faculty positions in statistics, engineering, business, and data science, and has also worked in applied research roles outside the university. This breadth of experience informs his commitment to advancing data science as both a rigorous scientific discipline and a tool for solving real-world problems.

Porter’s research focuses on the statistical modeling of event data, with contributions in three methodological areas: event prediction and forecasting, pattern and anomaly detection, and data linkage. His work applies these approaches to a wide range of domains, including transplant medicine, transportation safety, criminology, environmental science, and security. While rooted in the statistical foundations of data science, his research emphasizes interpretability and practical impact in decision-making contexts.

He directs the Event Analytics and Statistical Learning Lab at the University of Virginia, where he works with students and collaborators to develop and apply event-based statistical and machine learning methods. The lab’s projects extend from point process modeling to anomaly detection and data linkage, with applications that advance both methodological innovation and societal impact.

Porter holds a Ph.D. in Systems and Information Engineering from University of Virginia, an M.S. in Systems Engineering from Vanderbilt University, and a B.S. in Industrial Engineering from Purdue University. He completed a postdoc in Statistics at North Carolina State University and SAMSI.