Rate My Professor Mike Wojnowicz

Michael Wojnowicz is an Assistant Professor and Hambly Endowed Chair in the Gianforte School of Computing at Montana State University, where he specializes in probabilistic machine learning within the Computer Science faculty. He earned his Ph.D. in Cognitive Science from Cornell University, receiving the Dallenbach Fellowship for Excellence in Experimental Research, the Cognitive Science Dissertation Proposal Award, and the Cognitive Science Experimental Research Award in 2008 and 2009. After his doctorate, he pursued degrees in mathematics and statistics and worked for six years as a Distinguished Data Scientist at Cylance, a cybersecurity startup, developing probabilistic machine learning models to detect malicious computer files and anomalous user activity from real-time sensors. This industry experience led to 11 U.S. patents, including first-author patents for "Detecting malware with deep generative models" awarded in 2023 and "Bayesian continuous user authentication" awarded in 2023. He then served as a Data Scientist II and Postdoctoral Researcher in the Machine Learning group at Tufts University's Data Intensive Studies Center, followed by a Research Associate position in the Department of Biostatistics at Harvard University from September 2023 to 2024, advised by Dr. Jeffrey Miller.

Wojnowicz's research lies at the intersection of statistics and machine learning, focusing on scalable Bayesian inference, time series modeling, changepoint detection across multiple samples, and methods that blend interpretable probabilistic models with techniques for handling large, complex datasets. His applications include cybersecurity, cancer research via copy number alterations, stroke recovery prediction, and soldier performance analysis. Key publications include "Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models" (Transactions in Machine Learning Research, 2025), "Easy Variational Inference for Categorical Models via an Independent Binary Approximation" (International Conference on Machine Learning, 2022, spotlight talk), "Approximate inference by broadening the support of the likelihood" (Symposium on Advances in Approximate Bayesian Inference, 2023), and "Influence sketching: Finding influential samples in large-scale regressions" (IEEE International Conference on Big Data, 2016). At Montana State University, starting January 2025, he contributes to the SMART FIRES project on sensors, machine learning, and AI for real-time fire science, teaches classes on probabilistic data modeling, and seeks students for probabilistic machine learning research. He has presented at the New England Statistics Symposium (2024) and given invited talks, such as at Harvard's Data to Actionable Knowledge Lab (2022).