Utilizing his training in statistics and computer science, Slawski combines perspectives and skill sets unique to each field to address contemporary data science challenges in engineering, society and health.  

Slawski has published broadly in leading statistics and computer science venues on a variety of topics, including high-dimensional data analysis, data compression, dimensionality reduction, biometric recognition and data integration. The National Science Foundation and other federal agencies such as the National Institute of Justice and the National Institutes of Health have funded his research. He was a Summer at Census Scholar in 2019 and 2024 and currently serves as an associate editor of the Electronic Journal of Statistics. 

Slawski holds a Diplom degree (equivalent to a combined bachelor’s and master’s degree) in statistics from Ludwig-Maximilians-University of Munich and a doctoral degree in computer science from Saarland University (Germany). His academic career in the United States began with a postdoctoral fellowship at Rutgers University. Before joining the University of Virginia, Slawski was an assistant and then associate professor in George Mason University’s Department of Statistics and held visiting positions at Columbia University and Baidu Research USA. 

A current focus of his research concerns methodology and computational tools for data integration and record linkage in the presence of linkage uncertainty and connections to data privacy. This fall, Slawski will teach a graduate course covering advanced topics in optimization and computational statistics.