Michael Schweinberger (Ph.D., University of Groningen, NL) serves as an Associate Professor of Statistics at the Pennsylvania State University, with a courtesy appointment at the University of Missouri-Columbia. Prior to coming to the Pennsylvania State University, he served on the faculty of Rice University and held postdoctoral positions at the Pennsylvania State University and the University of Washington, Seattle.
- Jonathan R. Stewart, Assistant Professor, Department of Statistics, Florida State University
- Sergii Babkin, Senior Data & Applied Scientist, Microsoft
Schweinberger's research is motivated by discrete and dependent network, spatial, and temporal data arising in the social sciences and other fields. While statistics - the science of learning from observation - provides the guiding principles for updating knowledge about these and other phenomena, data on social and economic phenomena give rise to unique statistical challenges: More often than not, social and economic phenomena are interconnected and interdependent and repeating experiments under identical conditions is challenging or impossible. As a consequence, Schweinberger's research focuses on three related areas of research, which inform each other:
- the mathematical foundations of learning from discrete and dependent data without independent replications;
- the development of scalable computational and statistical methods;
- the design of probability models that respect the complexity of the social and economic universe.
His research has been funded by the U.S. National Science Foundation, in the form of NSF awards DMS-1812119 (sole PI) and DMS-1513644 (sole PI); the U.S. Department of Defense, in the form of ARO award W911NF-21-1-0335 (lead PI); and Netherlands Organisation for Scientific Research (NWO), in the form of NWO award Rubicon-44606029 (sole PI).
Selected research publications
Stewart, J.R. and M. Schweinberger (2022). Pseudo-likelihood-based M-estimation of random graphs with dependent edges and parameter vectors of increasing dimension. arXiv:2012.07167
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, Sergii, Stewart, Jonathan R., Long, Xiaochen, and Michael Schweinberger (2020). Large-scale estimation of random graph models with local dependence. Computational Statistics & Data Analysis, 152, 1–19.
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.
Schweinberger, M. (2011). Instability, sensitivity, and degeneracy of discrete exponential families. Journal of the American Statistical Association, Theory & Methods, 106, 1361–1370.
Snijders, Tom A.B., Koskinen, Johan, and Michael Schweinberger (2010). Maximum likelihood estimation for social network dynamics. The Annals of Applied Statistics, 4, 567–588.
Honors and Awards
- NSF award DMS-1812119 (sole PI)
- NSF award DMS-1513644 (sole PI)
- DoD award ARO award W911NF-21-1-0335 (lead PI)
- NWO award Rubicon-44606029 ("Dutch NSF") (sole PI)
- William D. Richards Software Award of the International Network for Social Network Analysis awarded to the core development team of the statistical software package (R)Siena (member 2002–2007)
In addition to serving on the Editorial Board of the Journal of Computational and Graphical Statistics, the Journal of Statistical Software, and Computational Statistics & Data Analysis, Schweinberger served as a panelist and reviewer for U.S. and European academic and governmental institutions, including the National Academies of Sciences, Engineering and Medicine (NASEM), the National Science Foundation (NSF), the European Research Council ERC (“European NSF”), the German Research Foundation DFG ("German NSF"), and the Netherlands Organisation for Scientific Research NWO (“Dutch NSF”).
STAT 597 Statistical learning with networks
STAT 416 & MATH 416 Stochastic modeling