Annie Qu is Chancellor’s Professor, Department of Statistics, University of California, Irvine. 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. The newly developed methods can extract essential and relevant information from large volumes of intensively collected data, such as mobile health data. Her research impacts many fields, including biomedical studies, genomic research, public health research, social and political sciences. Before joining UC Irvine, 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. She was awarded the Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC and was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the IMS, ASA, and the American Association for the Advancement of Science. She serves as Journal of the American Statistical Association, Theory and Methods Co-Editor from 2023 to 2025 and as IMS Program Secretary from 2021 to 2027. This Medallion lecture will be delivered at JSM in Portland, USA, August 3–8, 2024: https://ww2.amstat.org/meetings/jsm/2024/index.cfm

Data Integration for Heterogeneous Data

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 methods such as data fusion, optimal transport, missing data imputations, and matrix completions. Additionally, we will introduce a new latent representation method 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 of the proposal is to project and align heterogeneous raw observations on latent spaces, and the novelty of our method is that we can directly align the high-order relations among variables in latent space instead of aligning the raw data itself. The main advantages of the proposed method are that it can increase statistical power in identifying common patterns by reducing heterogeneity unrelated to the signal and aligning the extracted latent information across subjects. 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. The major distinctions of this proposal from other works are that we are able to incorporate individual variation from general populations and integrate multiple sources of high-dimensional data information to enhance prediction precision and interpretability.