Professor of Econometrics and Statistics, and James S. Kemper Foundation Faculty Scholar
Professor Rockova's research brings together statistics and machine learning to develop tools for learning from large datasets. She specializes in topics at the intersection of Bayesian and frequentist statistics, including: variable selection, uncertainty quantification, Bayesian nonparametrics, factor and dynamic models, and high-dimensional decision theory and inference.
She has published a variety of works in esteemed journals including the Journal of the American Statistical Association, the Journal of the Royal Statistical Society or the Annals of Statistics. Her applied work has contributed to the improvement of patient risk stratification and public reporting in healthcare analytics. Her research was recognized by the prestigious CAREER Award for early-career faculty by the National Science Foundation in 2020. She is currently on the Editorial Board of the Annals of Statistics.
Prior to joining Booth, Rockova held a Postdoctoral Research Associate position at the Department of Statistics of the Wharton School at the University of Pennsylvania. Rockova holds a PhD in biostatistics from Erasmus University (The Netherlands), an MSc in biostatistics from Universiteit Hasselt (Belgium) and both an MSc in mathematical statistics and a BSc in general mathematics from Charles University (Czech Republic).
Beyond statistics, she is a keen piano and tennis player.
| Number | Course Title | Quarter |
|---|---|---|
| 41201 | Big Data | 2024 (Spring) |
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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