Born in Czechoslovakia, and educated in the Czech Republic, Belgium, and the Netherlands, she works in the US as a professor of econometrics and statistics and James S. Kemper Faculty Scholar at the University of Chicago.
[1] Her research studies methods including variable selection, high-dimensional inference, non-convex optimization, likelihood-free inference, and the spike-and-slab LASSO, and also includes applications in biomedical statistics.
[2] Her doctoral dissertation, Bayesian Variable Selection in High-dimensional Applications, was supervised by Emmanuel Lesaffre.
[2] Ročková was the 2018 Susie Bayarri Lecturer of the International Society for Bayesian Analysis.
[6] She was a recipient of the 2023 Emerging Leader Award[7] and the recipient of the 2024 COPSS Presidents' Award of the Committee of Presidents of Statistical Societies, given "for path-breaking contributions to theory and methodology at the intersection of Bayesian and frequentist Statistics in the areas of variable selection, factor models, non-parametric Bayes, tree-based and deep-learning methods, high-dimensional inference, generative methods for Bayesian computation; for exemplary service to Statistics and for generous mentorship of students and post-doctoral researchers".