Michael Schweinberger

Professor of Statistics
Michael Schweinberger
Michael
Schweinberger
Professor of Statistics
Address
Website
Publications

Biography

Michael Schweinberger (Ph.D., University of Groningen, NL) is a Professor of Statistics at The Pennsylvania State University. In the past, he served on the faculty of Rice University, held visiting positions at the University of Washington, Seattle and the University of Missouri, Columbia, and held postdoctoral positions at The Pennsylvania State University and the University of Washington, Seattle.

Ph.D. Students and Postdoctoral Scholars

  • Subhankar Bhadra, Postdoctoral Scholar. Department of Statistics, Pennsylvania State University
  • Cornelius Fritz, Postdoctoral Scholar. First position: tenure-track Assistant Professor, School of Computer Science and Statistics, Trinity College Dublin, University of Dublin, Ireland
  • Jonathan R. Stewart, Ph.D. student. First position: tenure-track Assistant Professor, Department of Statistics, Florida State University
  • Sergii Babkin, Ph.D. student. First position: Data & Applied Scientist, Microsoft
  • Served on 17 PhD committees

Research

Since the emergence of agriculture, economic trade, writing, and mathematics during the last 10,000 years, networks have pervaded science and technology and affect the welfare of billions of people around the world. 

Examples include networks of artificial neurons used in artificial intelligence to construct intelligent machines; networks of neurons used to study how the human brain operates; networks of interacting genes used to study genetic diseases; networks of contacts that enable infectious diseases to spread and affect the health of billions of people; social networks used to study how information and disinformation spread through social media; and networks of economic and financial transactions, which affect the economic welfare of billions of people.

Schweinberger's research on the statistical science of networks has been funded by the U.S. National Science Foundation (NSF), the U.S. Department of Defense (DoD), and the Netherlands Organisation for Scientific Research (NWO).

Selected Research Publications

Bhadra, S. and M. Schweinberger. Characterizing direct and indirect causal effects when outcomes are dependent due to treatment spillover and outcome spillover. arXiv:2504.06108

Fritz, C., Schweinberger, M., Bhadra, S., and David R. Hunter. A regression framework for studying relationships among attributes under network interference. arXiv:2410.07555

Stewart, J.R. and M. Schweinberger. Pseudo-likelihood-based M-estimation of random graphs with dependent edges and parameter vectors of increasing dimension. arXiv:2012.07167

Eli, S. and Schweinberger, M. (2024). Non-asymptotic model selection for models of network data with parameter vectors of increasing dimension. Journal of Statistical Planning and Inference, 233, 106173.

Jeon, M. and M. Schweinberger (2024). A latent process model for monitoring progress towards hard-to-measure targets, with applications to mental health and online educational assessments. The Annals of Applied Statistics, 18, 2123-2146.

Schweinberger, M., Bomiriya, R.P., and S. Babkin (2022). A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015. Journal of Nonparametric Statistics, 34, 628–662.

Jeon, M., Jin, I.H., Schweinberger, M., and S. Baugh (2021). Mapping unobserved item-respondent interactions: A latent space item response model with interaction map. Psychometrika, 86, 378–403.

Schweinberger, M. and J.R. Stewart (2020). Concentration and consistency results for canonical and curved exponential-family models of random graphs. The Annals of Statistics, 48, 374–396.

Schweinberger, M. (2020). Consistent structure estimation of exponential-family random graph models with block structure. Bernoulli, 26, 1205–1233.

Schweinberger, M., Krivitsky, P.N., Butts, C.T., and J.R. Stewart (2020). Exponential-family models of random graphs: Inference in finite, super, and infinite population scenarios. Statistical Science, 35, 627–662.

Babkin, S., Stewart, J. R., Long, X., and M. Schweinberger (2020). Large-scale estimation of random graph models with local dependence. Computational Statistics & Data Analysis, 152, 1–19.

Schweinberger, M. (2019). Random graphs. Wiley StatsRef: Statistics Reference Online. Edited by B. Everitt, G. Molenberghs, W. Piegorsch, F. Ruggeri, M. Davidian, and R. Kenett. Invited.

Schweinberger, M. and P. Luna (2018). hergm: Hierarchical exponential-family random graph models. Journal of Statistical Software, 85, 1–39.

Schweinberger, M., Babkin, S., and K.B. Ensor (2017). High-dimensional multivariate time series with additional structure. Journal of Computational and Graphical Statistics, 26, 610–622.

Schweinberger, M. and M.S. Handcock (2015). Local dependence in random graph models: Characterization, properties and statistical inference. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 77, 647–676.

Vu, D.Q., Hunter, D.R., and M. Schweinberger (2013). Model-based clustering of large networks. The Annals of Applied Statistics, 7, 1010–1039.

Hunter, D.R., Krivitsky, P.N., and M. Schweinberger (2012). Computational statistical methods for social network models. Journal of Computational and Graphical Statistics, 21, 856-882. Invited.

Schweinberger, M. (2011). Instability, sensitivity, and degeneracy of discrete exponential families. Journal of the American Statistical Association, Theory & Methods, 106, 1361–1370.

Snijders, T.A.B., Koskinen, J., and M. Schweinberger (2010). Maximum likelihood estimation for social network dynamics. The Annals of Applied Statistics, 4, 567–588.

Service

In addition to serving on the Editorial Board of the Journal of Computational and Graphical Statistics, the Journal of Statistical Software, Computational Statistics & Data Analysis, Econometrics & Statistics, and Statistical Methods & Applications (Guest Editor), Schweinberger served as a panelist and reviewer for U.S. and European academic and governmental institutions, including the U.S. National Academies of Sciences, Engineering and Medicine (NASEM), the U.S. National Science Foundation (NSF), the European Research Council (ERC), the German Research Foundation (DFG), and the Netherlands Organisation for Scientific Research (NWO).

Teaching

STAT 597 Statistical learning with networks

STAT 416 & MATH 416 Stochastic modeling

STAT 415 & MATH 415 Introduction to mathematical statistics