Jelena Bradic

Professor, Statistics and Data Science
Cornell University
Fellow, Center for Data Science for Enterprise and Society

Before joining Cornell University, she held positions at the University of California, San Diego, in both the Department of Mathematics and the Halıcıoğlu Data Science Institute. She earned her Ph.D. in Statistics from the Department of Operations Research and Financial Engineering at Princeton University, where she studied under Professor Jianqing Fan. Prior to that, she completed her B.S. and M.S. degrees in Mathematics at the University of Belgrade in Serbia.

RESEARCH INTERESTS

Her research lies at the intersection of causal inference, robustness, and high-dimensional statistics, with a focus on developing theoretically grounded methods that remain valid under minimal and realistic assumptions. In causal inference, she investigates optimal procedures for estimating average treatment effects (ATE) under weak identification, addresses the complexities of dynamic treatment regimes with multiple or time-varying interventions, and develops frameworks for individualized policy learning that target heterogeneity in treatment timing and assignment. In robustness, her work aims to systematically relax classical assumptions, allowing for model misspecification, sparsity violations, and distributional shifts, by constructing estimators and learning algorithms that are minimax optimal or adaptively robust. This includes the development of novel machine learning methods guided by statistical theory. She also studies principled approaches to missing data, including semi-supervised and censored data structures, within both inferential and predictive frameworks. She is interested in understanding the implications of modern AI models for causality and robustness, and in formulating theoretical principles that enable the responsible and reliable deployment of such systems in high-stakes domains.

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