Veronika Rockova’s work brings together statistical methodology, theory, and computation to develop high-performance tools for analyzing large datasets. Her research interests reside at the intersection of Bayesian and frequentist statistics, and focus on data mining, variable selection, optimization, non-parametric methods, factor models, high-dimensional decision theory, and inference. In her applied work, she has contributed to the development of risk stratification and prediction models for public reporting in health care analytics.
Rockova’s research has been published in the Journal of the American Statistical Association, Theory and Methods; Annals of Statistics; and Metron. She received first honorable mention for the Savage Award, given by the International Society of Bayesian Analysis for the outstanding doctoral thesis in Bayesian methodology and theory.
Rockova holds a BSc in general mathematics and an MSc in mathematical statistics from Charles University in the Czech Republic, as well as an MSc cum laude in biostatistics from Universiteit Hasselt in Belgium. She earned a PhD cum laude in biostatistics from Erasmus University in the Netherlands. Previously, she held a postdoctoral research associate position at the Department of Statistics of the Wharton School of the University of Pennsylvania.