Research Highlights
Veronika Rockova for
I specialize in transforming uncertainty into understanding — using data science and applied AI to uncover patterns, improve decisions, and build systems where humans and machines think better together.
My research has received international recognition, including the 2020 NSF CAREER Award, the 2023 COPSS Emerging Leader Award, and the 2024 COPSS Presidents’ Award — the highest honor for a statistician under the age of 40 — for pioneering contributions at the intersection of statistics and machine learning.
AI-Powered Bayes
The Federalist and LLMs
Deep CATs
Tree Bandits
Deep Bayes Factors
Flow Matching for Bayes
IQ-BART
Bayesian Predictive Inference
Generative Filtering
Generative Bayes with GANs
Uncertainty Quantification for Generative AI
Jinwon Sohn
Jinwon Sohn is a Principal Researcher in Econometrics and Statistics at the University of Chicago Booth School of Business. His research integrates statistical perspectives with cutting-edge data science methodologies. He currently focuses on developing novel approaches in areas such as generative modeling, fairness-aware machine learning, and differential privacy. Prior to his doctoral studies, he worked as a data scientist at Datarize. He earned his Ph.D. in Statistics from Purdue University under the supervision of Dr. Qifan Song. He also holds both an M.A. and a B.A. in Applied Statistics from Yonsei University with Dr. Taeyoung Park’s guidance.
Sowon Jeong
Sowon is a third-year Ph.D. student in Econometrics and Statistics at the University of Chicago Booth School of Business. Her research interests lie at the intersection of machine learning and statistics, with a focus on developing statistically principled methods for integrating machine learning into statistical inference. Recent projects include developing flow matching methods for scalable Bayesian posterior sampling and comparing large language model embeddings to classical probabilistic language models on historical authorship benchmarks. Sowon holds an M.S. in Statistics from the University of Chicago and a B.S. in Applied Statistics from Yonsei University.
Jiguang Li
Jiguang Li is a 4th year PhD student in Econometrics and Statistics at the University of Chicago Booth School of Business. His research interests span Bayesian statistics, reinforcement learning, and psychometrics. His work develops scalable Bayesian methodologies that go beyond traditional MCMC sampling, often motivated by real-world problems in latent-variable modeling. He is also interested in sequential decision problems that bridge Bayesian inference, reinforcement learning, and Bayesian optimization. Previously, he worked as a full-time research professional at the Center for Applied AI under the supervision of Sendhil Mullainathan. Jiguang holds a master's degree in statistics from Yale and a bachelor's degree in mathematics from Middlebury College.
Bengusu Nar
Bengüsu is a second-year Ph.D. student in Econometrics and Statistics at Booth. Her research interests are Bayesian and machine learning frameworks for understanding complex, dynamic systems, with applications to policy and social decision-making. She holds a B.Sc. in Economics and Data Science from Bocconi University.
Percy Zhai
Percy is a 5th-year PhD student in Econometrics and Statistics at Booth. His research interest encompasses Bayesian predictive inference, simulation-based inference, and time series. Percy obtained his M.S. degree in statistics from the University of Chicago, and B.S. degree in physics from University of Science and Technology of China.
Sean O'Hagan
Sean is a third year PhD student in statistics at the University of Chicago, supported by a McCormick fellowship. He is interested in Bayesian methodology, online learning, and applications to the physical and social sciences. His research currently focuses on using modern tools to accelerate likelihood-free Bayesian inference. Sean obtained a B.S. in mathematics and in statistics at the University of Connecticut.
Edoardo Marcelli
Edoardo is a third-year Ph.D. student in Econometrics and Statistics at the University of Chicago Booth School of Business. His research interests encompass Bayesian statistics, machine learning, and financial econometrics. He holds an M.S. in Economics and Social Sciences from Bocconi University and a B.S. in Economics from Roma Tre University.
Data Intelligence is an MBA course at Chicago Booth that is designed for students who are data hungry and who want to understand how AI is shaping business analytics.
The course prioritizes data storytelling–the ability to interpret, analyze, and communicate insights from data. Students learn how to craft compelling narratives grounded in data and build prediction systems with confidence.
