Jon A. Wellner Lecture
The Fall 2024 Jon A. Wellner Lecture
Tuesday, October 15, 2024
TLC 030 from 2:30 to 3:20 p.m.
*Reception following from 3:30 to 4:30 p.m. in the IRIC atrium. All are welcome, and drinks and refreshments will be provided.
Title
Data Integration for Heterogeneous Data
Speaker
Annie Qu, Ph.D., Chancellor's Professor, Department of Statistics, University of California, Irvine
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 methods such as multi-task learning, optimal transport, missing data imputations, matrix completions and transfer learning. 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 heterogeneous raw observations to the 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 common 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 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 analysis 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 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 and as IMS Program Secretary from 2021 to 2027.
Qu Lab website: https://faculty.sites.uci.edu/qulab
About the Jon A. Wellner Lecture
The Jon A. Wellner Lecture was established by Jon and Vera Wellner to provide educational experiences outside the classroom for students and faculty and to help to raise the profile of the University of Idaho by bringing well-known experts in the fields of statistics and probability to Moscow.