Statistical Learning in the Wild: Rethinking Discovery in the AI and Data Era | Jingyi Jessica Li

Описание: Recorded on 11/07/2025
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Title: Statistical Learning in the Wild: Rethinking Discovery in the AI and Data Era

Speaker: Jingyi Jessica Li

Abstract: Balancing false discovery rate (FDR) control with high statistical power remains a central challenge in high-dimensional variable selection. While several FDR-controlling methods have been proposed, many degrade the original data -- by adding knockoff variables or splitting the data -- which often leads to substantial power loss and hampers detection of true signals. We introduce Nullstrap, a novel framework that controls FDR without altering the original data. Nullstrap generates synthetic null data by fitting a null model under the global null hypothesis that no variables are important. It then applies the same estimation procedure in parallel to both the original and synthetic data. This parallel approach mirrors that of the classical likelihood ratio test, making Nullstrap its numerical analog. By adjusting the synthetic null coefficient estimates through a data-driven correction procedure, Nullstrap identifies important variables while controlling the FDR. We provide theoretical guarantees for asymptotic FDR control at any desired level and show that power converges to one in probability. Nullstrap is simple to implement and broadly applicable to high-dimensional linear models, generalized linear models, Cox models, and Gaussian graphical models. Simulations and real-data applications show that Nullstrap achieves robust FDR control and consistently outperforms leading methods in both power and efficiency.

Part of the NITMB Seminar Series

The NSF-Simons National Institute for Theory and Mathematics in Biology Seminar Series aims to bring together a mix of mathematicians and biologists to foster discussion and collaboration between the two fields. The seminar series will take place on Fridays from 10am - 11am at the NITMB in the John Hancock Center in downtown Chicago. There will be both an in-person and virtual component.

The NSF-Simons National Institute for Theory and Mathematics in Biology (NITMB) aims to integrate the disciplines of mathematics and biology in order to transform the practice of biological research
and to inspire new mathematical discoveries. NITMB is a partnership between Northwestern University and the University of Chicago. It is funded by the National Science Foundation DMS-2235451 and the Simons Foundations MP-TMPS-00005320.

The mission of the NITMB is to create a nationwide collaborative research community that will generate new mathematical results and uncover the “rules of life” through theories, data-informed mathematical models, and computational and statistical tools. The NITMB leverages close collaborations between experimentalists and theorists to synergize discovery. The fundamental research done by NITMB will stimulate advances in areas as diverse as the environment, medicine, and technology development. NITMB members and visitors share space in downtown Chicago that is readily accessible to collaborators across the U.S. and the world.

NITMB uses an interlocking set of strategies and initiatives aimed at broad impacts for the mathematical and biological research communities. Targeted research bringing together mathematicians and biologists to collaborate and train the next generation of interdisciplinary scientists. Scientific long programs, workshops, and conferences enhancing collaboration between mathematics and biology. An innovative research program organized around five interrelated themes, selected because they reflect key capabilities of biological systems and interconnect with open mathematical problems.