Department of Statistics
Regina Liu is Distinguished Professor of Statistics at Rutgers University. Her research areas include data depth, resampling, confidence distribution, and fusion learning. Aside from theoretical and methodological research, she has long collaborated with the FAA on aviation safety research projects on statistical process control, text mining and risk management. She has served as Co-Editor for the Journal of the American Statistical Association and the Journal of Multivariate Analysis, and as Associate Editor for several journals, including the Annals of Statistics. She is an elected fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected member of the International Statistical Institute. Among other distinctions, she is the recipient of 2021 Noether Distinguished Scholar Award (American Statistical Association), 2011 Stieltjes Professorship (The Netherlands), and has delivered an IMS Medallion Lecture. She was elected President of the Institute of Mathematical Statistics, 2020-2021.
Advanced data collection technology nowadays often makes inferences from diverse data sources easily accessible. Fusion learning refers to combining inferences from multiple sources or studies to make more effective inference than from any individual source or study alone. We focus on the tasks: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently if you need to?
We present a general framework for nonparametric and efficient fusion learning for inference on multi-parameters, which may be correlated. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD), which is 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 shown to be efficient, general and robust. Specifically, it achieves high-order accuracy and Bahadur efficiency under suitably chosen combining elements. It readily applies to heterogeneous studies with a broad range of complex and irregular settings. This property also enables the approach to utilize indirect evidence from incomplete studies to gain efficiency for the overall inference.
This is joint work with Dungan Liu of University of Cincinnati and Minge Xie of Rutgers University.
Global Science Leader, AI for Healthcare
Director of HCLS Research at IBM Research
Jianying Hu (PhD) is IBM Fellow; Global Science Leader, AI for Health care; and Director of HCLS Research at IBM Research. Prior to joining IBM in 2003 she was with Bell Labs at Murray Hill, New Jersey. Dr. Hu has conducted and led extensive research in machine learning, data mining, statistical pattern recognition, and signal processing, with applications to health care analytics and medical informatics, business analytics, and multimedia content analysis. Her recent focus has been on leading research efforts to develop advanced computational methods for deriving data-driven insights from real world health care data. Dr. Hu served as Chair of the Knowledge Discovery and Data Mining (KDDM) Working Group of the American Medical Informatics Association (AMIA) from 2014 to 2016, and on the Computational Science Advisory Board of Michael J. Fox Foundation from 2017 to 2018. She has published over 140 peer reviewed scientific papers and holds 44 patents. She has served as Associate Editor for the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, Pattern Recognition, and International Journal for Document Analysis and Recognition, and currently serves on the Editorial Board of the journal JAMIA Open, the Advisory Board of the Journal of Health care Informatics Research, and the External Advisory Board of Vanderbilt University Department of Biomedical Informatics. Dr. Hu is a fellow of the American College of Medical Informatics (ACMI), International Academy of Health Sciences Informatics (IAHSI), IEEE, and the International Association of Pattern Recognition (IAPR). She received the Asian American Engineer of the Year Award in 2013.
Large amount of health care and life sciences data of different types has become increasingly available: clinical encounters, lab results, diagnostics, medications, genomics, and increasingly, physiological, lifestyle, social behavioral and environmental data. AI for Health care and Life Sciences is all about enabling the journey from such data to meaningful insights to drive improved health and wellness outcomes. One particularly area what AI can make significant impact is in the accelerated discovery of therapeutics and biomarkers. At IBM Research we have been systematically developing advanced artificial intelligence and data science methodologies for health care and life sciences, ranging from intelligent data preparation and pattern extraction to complex models for actionable insights generation. These methodologies have been applied to a wide range of use cases and disease areas. I will discuss these methods and use cases, lessons learned and important future directions.
H.C. Carver Professor of Statistics
Department of Statistics
University of Michigan
Xuming He is H.C. Carver Collegiate Professor of Statistics, University of Michigan. He currently serves as President-Elect of the International Statistical Institute (ISI).
He is an internationally recognized leader in statistics who has published extensively on robust and trustworthy statistical inference as well as applications to biosciences, climate studies, and concussion research. He is an elected Fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the American Association for the Advancement of Science (AAAS). He has been honored by the Founders Award (2021) from the ASA, Harry C. Carver Medal (2022) from the IMS, Distinguished Achievement Award (2015) from the International Chinese Statistical Association, and the IMS Medallion Lectureship (2007).