HDSI Seminar – Regina Liu – Fusion Learning: Fusing Inferences from Diverse Data Sources

HDSI will be hosting its first Seminar Series speaker of the academic year at the end of this month. Regina Liu (Rutgers University) will be giving a talk Monday Sept 29th at 2pm in the HDSI Multipurpose room, 1st floor Room 123. 

Speaker: Regina Liu

Date & Time: Monday Sept 29th, 2pm

Location: HDSI Multipurpose Room 123, 1st floor

Talk Title: Fusion Learning: Fusing Inferences from Diverse Data Sources

Advanced data acquisition technology has greatly increased the accessibility of complex inferences, based on summary statistics or sample data, from diverse data sources. Fusion learning refers to combining complex inferences from multiple sources to yield a more effective overall. We focus on the tasks: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently? 3) How to combine inferences to enhance an individual study, thus named i-Fusion?

We present a general framework for nonparametric and efficient fusion learning. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD), developed by combining data depth, bootstrap and confidence distributions. We show that a depth-CD is an omnibus form of confidence regions, whose contours of level sets shrink toward the true parameter value, and thus an all-encompassing inferential tool. The approach is efficient, general and robust, and readily applies to heterogeneous studies covering a broad range of complex settings. The approach is demonstrated with an aviation safety analysis application in tracking aircraft landing performance and a zero-event studies in clinical trials with non- estimable parameters.

Key words: confidence distribution, data depth, fusion learning, heterogeneous studies

Speaker Bio:

Regina Liu is Distinguished Professor, Rutgers University. Her research areas include data depth, resampling, nonparametric statistics, confidence distribution, and fusion learning. Aside from theoretical and methodological research, she has long collaborated with the FAA on aviation safety research projects on process control, text mining and risk management.

She is an elected fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). She is the recipient of 2021 Noether Distinguished Scholar Award (ASA), 2024 Elizabeth Scott Award (Committee of Presidents of Statistical Societies (COPSS)), and the IMS 2025 Neyman Award & Lecture. She has served as Co-Editor for the Journal of the American Statistical Association and as Associate Editor for several journals. She was elected President of the Institute of Mathematical Statistics (IMS), 2020-2021.