ABSTRACT  This talk seeks to redefine the boundaries of statistical robustness. For too long, the field has languished in the shadows of contamination models, adversarial constructs, and outlier management—approaches that, while foundational, scarcely scratch the surface of potential that model misspecification offers. Our research reveals a fundamental link between robustness and causality, initiating an innovative era in data science. This era is defined by how causality enhances robustness, and in turn, how effectively applied robustness opens up unprecedented opportunities for scientific exploration.

BIO  Jelena Bradic is a Professor at the University of California, San Diego, where she specializes in statistics and data science within the Department of Mathematics and the Halicioglu Data Science Institute. Her research focuses on developing robust statistical methods that are resistant to model misspecification, with particular emphasis on high-dimensional data analysis, causal inference, and machine learning applications. Bradic holds a Ph.D. from Princeton University and has made significant contributions to the field of statistics. She is the co-editor in Chief of the first interdisciplinary journal between ACM and IMS named ACM/IMS Journal of Data Science, and  the recipient of several prestigious awards, including the  Wijsman Lecture (2023), a Discussion Paper in the Journal of the American Statistical Association (2020) and a Hellman Fellowship, recognizing her as a leading figure in statistical science.

This talks is part of the Cornell Center for Data Science for Enterprise and Society's  Data Science Distinguished Lecture series and co-sponsored with the Department of Statistics and Data Science.