Michael T. Wojnowicz

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Hi! I’m Assistant Professor and Hambly Endowed Chair in the School of Computing at Montana State University. Previously, I was a research associate at Harvard University in the Department of Biostatistics, advised by Dr. Jeffrey Miller. Prior to that, I was a postdoctoral researcher with Tufts University Machine Learning, advised by Computer Science Professor Michael C. Hughes, as well as a Data Scientist at Tufts’ Data Intensive Studies Center.

I’m interested in statistical machine learning, particularly scalable Bayesian inference, time series modeling, and methodologies using measure-theoretic probability.

news

Jul 18, 2025 Our paper, Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models, has been accepted for publication in TMLR 2025.
Jan 01, 2025 I have started a position as assistant professor in the Gianforte School of Computing at Montana State University.
May 24, 2024 I presented new work Scalable Bayesian multi-sample changepoint modeling at the Recent Advances in Variational Inference session of the 2024 New England Statistics Symposium.
Sep 05, 2023 I have started a new position as a research associate at Harvard University in the Department of Biostatistics. I will be developing statistical methods and theory for finding patterns in complex biomedical data, under the direction of Dr. Jeffrey Miller.
Jun 20, 2023 Our paper Approximate inference by broadening the support of the likelihood has been accepted for presentation at the 5th Symposium on Advances in Approximate Bayesian Inference. This is joint work with Assistant Professor Michael C. Hughes and Mathematics Ph.D. student Martin Buck.
Apr 25, 2023 I have been awarded a U.S. patent for Detecting malware with deep generative models.
Jan 03, 2023 We have been awarded a U.S. patent for Bayesian continuous user authentication. This is joint work with Mathematician Dinh Nguyen, Ph.D., and Data Scientist Alexander Kohn, Ph.D.
Jul 29, 2022 Invited speaker at Harvard University’s Data to Actionable Knowledge Lab.
Jul 22, 2022 Spotlight talk at ICML 2022: Easy Variational Inference for Categorical Models via an Independent Binary Approximation. Recording is here.

selected recent publications

  1. TMLR
    Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models
    Michael Wojnowicz, Kaitlin Gili, Preetish Rath, and 7 more authors
    Transactions in Machine Learning Research, 2025
    PDF
  2. AABI
    Approximate inference by broadening the support of the likelihood
    Michael Wojnowicz, Martin D Buck, and Michael C Hughes
    In Fifth Symposium on Advances in Approximate Bayesian Inference, 2023
    PDF Poster
  3. ICML
    Easy Variational Inference for Categorical Models via an Independent Binary Approximation
    Michael Wojnowicz, Shuchin Aeron, Eric L Miller, and 1 more author
    In International Conference on Machine Learning, 2022
    PDF Poster
  4. UAI-TPM
    Easy Variational Inference for Categorical Observations via a New View of Diagonal Orthant Probit Models
    Michael Wojnowicz, Shuchin Aeron, Eric Miller, and 1 more author
    In The 4th Workshop on Tractable Probabilistic Modeling, 2021