Abstract:
In this presentation, I will showcase advanced statistical machine learning techniques and tools designed for the seamless integration of information from multi-source datasets. These datasets may originate from various sources, encompass distinct studies with different variables, and exhibit unique dependent structures. One of the greatest challenges in investigating research findings is the systematic heterogeneity across individuals, which could significantly undermine the power of existing machine learning methods to identify the underlying true signals. This talk will investigate the advantages and drawbacks of current data integration methods such as multi-task learning, optimal transport, missing data imputations, matrix completions and transfer learning. Additionally, we will introduce a new representation retriever learning aimed at mapping heterogeneous observed data to a latent space, facilitating the extraction of shared information and knowledge, and disentanglement of source-specific information and knowledge. The key idea is to project heterogeneous raw observations to representation retriever library, and the novelty of our method is that we can retrieve partial representations from the library for a target study. The main advantages of the proposed method are that it can increase statistical power through borrowing partially shared representation retrievers from multiple sources of data. This approach ultimately allows one to extract information from heterogeneous data sources and transfer generalizable knowledge beyond observed data and enhance the accuracy of prediction and statistical inference.
About the Speaker:
Annie Qu is Professor at Department of Statistics and Applied Probability, University of California, Santa Barbara starting July 2025. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. 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. Dr. Qu was 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. 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. She is also a recipient of IMS Medallion Award and Lecturer in 2024. She 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 the recipient of the 2025 Carver Medal of IMS.
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