Professor of Econometrics and Statistics, and James S. Kemper Faculty Scholar
Veronika Rockova is Professor of Econometrics and Statistics and the James S. Kemper Faculty Scholar at the Booth School of Business at the University of Chicago. She joined Booth after completing her postdoctoral training in statistics at the Wharton School of the University of Pennsylvania. She earned a bachelor’s degree in mathematics and a master’s degree in mathematical statistics from Charles University in Prague. Subsequently, she pursued a master’s degree in biostatistics at Hasselt University in Belgium, and later completed her doctoral degree in biostatistics at Erasmus University in Rotterdam. Her research interests lie at the intersection of statistics and machine learning, with a primary focus on creating innovative decision-centric tools for extracting insights from extensive datasets. She specializes in Bayesian computation, variable selection, high-dimensional decision theory, and hierarchical modeling.
After joining the Booth School of Business at the University of Chicago as an assistant professor in 2016, she broadened her research agenda to include theoretical aspects of popular Bayesian machine learning methods (such as Bayesian CART and BART). Leveraging machine learning for Bayesian computation has become a major theme in her more recent work. She was recognized by the NSF CAREER Award in 2020, the year she was promoted to associate professor. In 2022, she became a full professor and was recognized again in 2023 with the COPSS Emerging Leader Award. In 2024 she received the COPSS Presidents' Award (the best statistician under 40) for "path-breaking contributions to theory and methodology at the intersection of Bayesian and frequentist Statistics... for exemplary service to Statistics and for generous mentorship of students and post-doctoral researchers."
In her work, Veronika continues to champion Bayes on various fronts: (1) tackling computational challenges by developing new faster algorithms, (2) providing much needed theoretical justifications, (3) creating new methodology using the most current machine learning constructs. She currently serves on the editorial boards of the Annals of Statistics, Journal of the American Statistical Association and Journal of the Royal Statistical Society (Series B). At the University of Chicago, she participates in several initiatives that promote the role of Statistics in society (Centre of Applied AI at Booth, Committee on Quantitative Methods in Social, Behavioral and Health Sciences, Data Science Institute). She is also an affiliate faculty at the Department of Statistics at the University of Chicago.
Beyond her academic pursuits, Veronika is an avid pianist, tennis enthusiast, and golf neophyte.
| Number | Course Title | Quarter |
|---|---|---|
| 41201 | Big Data | 2025 (Winter) |
Machine learning is being tasked with an increasing number of important decisions. But the answers it generates involve a degree of uncertainty.
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The most influential rating system rests on some faulty calculations, affecting millions of people and billions of dollars.
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The US federal government’s hospital scoring can be woefully inaccurate, research suggests, giving many small hospitals undue boosts.
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