Nereo Lecture 2026: Annie Qu to address challenges in heterogeneous datasets

Inaugural Val Nereo Lecture: "Representation Retrieval Learning for Heterogeneous Data Integration"

Date: Monday, Feb. 16, 2026
Time: Reception from 3 to 4 p.m.; Lecture begins at 4 p.m.
Location: Reception held in Weniger 245; Lecture held in Weniger 151

The R2 framework integrates a representation learning module (the "representer") with a sparsity-induced machine learning model (the "learner"). Qu and her team introduce the notion of “integrativeness” for representers, characterized by the effective data sources used in learning representers, and propose a Selective Integration Penalty (SIP) to explicitly improve the property.

Theoretically, her team demonstrates that the R2 framework relaxes the conventional full-sharing assumption in multi-task learning, allowing for partially shared structures, and that SIP can improve the convergence rate of the excess risk bound.

Extensive simulation studies validate the empirical performance of the team's framework, and applications to two real-world datasets further confirm its superiority over existing approaches.

About the speaker: Annie Qu

Annie Qu is a professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Dr. Qu was a faculty member in Oregon State University's Department of Statistics from 1999 to 2008. She went on to become a Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign during her tenure in 2008-2019, and Chancellor's Professor at UC Irvine in 2020-2025.

Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms for personalized medicine, text mining, recommender systems, medical imaging data and network data analyses for complex heterogeneous data.

She was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association and the American Association for the Advancement of Science. Qu was also a recipient of IMS Medallion Award and Lecturer in 2024. She currently serves as Journal of the American Statistical Association Theory and Methods Co-Editor from 2023 to 2025, IMS Program Secretary from 2021 to 2027 and ASA Council of Sections of Governing Board Chair in 2025. She is also the recipient of the 2025 Carver Medal of IMS.

The Val Nereo Lecture Series is made possible through a generous gift from the Val Nereo Women in Applied Statistics Endowed Fund. This series is dedicated to highlighting the work of leading women in statistics and data science, with the goal of inspiring students and scholars — especially women — in our department and broader community.