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.
We'll illustrate the connection between model misspecification in high-dimensional learning and the absence of exact low-dimensional structures in data. We’ll continue by illustrating that powerful large-scale models need to be adjusted if we are to achieve robust and causal inferential goals. This highlights a cautionary tale: the simplicity of using off-the-shelf machine learning algorithms is marred by their potential to infuse our scientific discoveries with biases.
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 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. An esteemed educator, Bradic is renowned for her dynamic teaching style and has developed numerous courses that bridge the gap between theoretical statistics and data science. Beyond her academic achievements, Bradic is deeply committed to service within the statistical community. She is the co-editor in Chief of the first interdisciplinary journal between ACM and IMS named ACM/IMS Journal of Data Science. She has also played a pivotal role in the establishment of the Data Science Institute at UCSD.