Keynote Presentation Ⅰ : Causal Learning from Invariance: Exogeneity and Multiple Environments
Speaker: Dr. Jianqing Fan, Princeton University
Jianqing Fan is Frederick L. Moore Professor of Finance, Professor of Operations Research and Financial Engineering, Former Chairman of Department of Operations Research and Financial Engineering, and Director of the Committee of Statistical Studies at Princeton University, where he directs both financial econometrics and statistics labs. After receiving his Ph.D. from the University of California at Berkeley, he was appointed as assistant, associate, and full professor at the University of North Carolina at Chapel Hill (1989-2003), professor at the University of California at Los Angeles (1997-2000), professor and chair at Chinese University of Hong Kong, and professor at the Princeton University (2003--). He was the past president of the Institute of Mathematical Statistics and the International Chinese Statistical Association. He is the joint editor of Journal of American Statistical Association and was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, Econometrics Journal, Journal of Econometrics, and Journal of Business and Economics Statistics. His published work on statistics, machine learning, economics, finance, and computational biology has been recognized by The 2000 COPSS Presidents' Award, The 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellow in 2009, P.L. Hsu Prize in 2013, Royal Statistical Society Guy medal in silver in 2014, Noether Distinguished Scholar Award in 2018, Le Cam Award and Lecture in 2021, and election to Academician of Academia Sinica and follow of American Associations for Advancement of Science, Institute of Mathematical Statistics, American Statistical Association, and Society of Financial Econometrics. His research interests include high-dimensional statistics, data science, machine learning, financial economics, and computational biology.

Abstract: Most papers on the high-dimensional variable selection are based on exogenous assumptions. Yet, endogeneity arises easily in high-dimensional regression due to a large pool of regressors, and this causes the inconsistency of the penalized least-squares methods. To cope with the endogeneity, Fan and Liao (2014) introduced a focused GMM that learns important causal variables through the invariance of the exogeneity in the presence of many endogenous variables. This talk develops further causal learning from a multiple environments linear regression model in which data from multiple experimental settings are collected. The joint distribution of the response variable and covariate may vary across different environments, yet the conditional expectation of outcome given the unknown set of important variables is invariant across environments. We construct a novel environment invariant linear least squares (EILLS) objective function, a multiple-environment version of linear least squares that leverages the above conditional expectation invariance structure and heterogeneity among different environments to determine the true parameter. We establish non-asymptotic error bounds on the estimation error for the EILLS estimator in the presence of spurious variables. Moreover, we further show that the ℓ₀ penalized EILLS estimator can achieve variable selection consistency in high-dimensional regimes. These non-asymptotic results demonstrate the sample efficiency of the EILLS estimator and its capability to circumvent the curse of endogeneity in an algorithmic manner without any prior structural knowledge. (Joint work with Cong Fang, Yihong Gu, Tong Zhang)

Keynote Presentation Ⅱ : TBD
Speaker: Dr. Eric Tchetgen Tchetgen, University of Pennsylvania
Eric J. Tchetgen Tchetgen is The University Professor, Professor of Biostatistics at the Perelman School of Medicine and Professor of Statistics and Data Science at The Wharton School at the University of Pennsylvania. He co-directs the Penn Center for Causal Inference, which supports the development and dissemination of causal inference methods in Health and Social Sciences. He has published extensively on Causal Inference, Missing Data and Semiparametric Theory with several impactful applications ranging from HIV research, Genetic Epidemiology, Environmental Health and Alzheimer's Disease and related aging disorders. He is an Amazon scholar working with Amazon scientists on a variety of causal inference problems in the Tech industry space. Professor Tchetgen Tchetgen is an 2022 inaugural co-recipient of the newly established Rousseeuw Prize for statistics in recognition for his work in Causal Inference with applications in Medicine and Public Health.

Abstract: TBD

Keynote Presentation Ⅲ : TBA
Speaker: Dr. Ji-Hyun Lee, University of Florida
Ji-Hyun Lee is a Professor in the Department of Biostatistics at the University of Florida and is the Director of the Cancer Data Science Division at the University of Florida Health Cancer Center (UFHCC). Her role at UFHCC involves providing strategic leadership, administrative direction, and support in biostatistics and computational biology for cancer research. Prior to joining UF in 2018, she led the Biostatistics Shared Resource at the University of New Mexico Comprehensive Cancer Center for four years and was a faculty member in Biostatistics at the Moffitt Cancer Center for 11 years. Ji-Hyun earned her doctoral and master’s degrees in Biostatistics from the University of North Carolina.

As a biostatistical team scientist, she collaborates on cancer research and leads large-scale grants, including U54 and P30, as a leader in various Biostatistical/Bioinformatics Cores. Her research interests encompass group randomized trials, clinical trial designs, biomarker studies, and best statistical practices.

Ji-Hyun is a fellow of the American Statistical Association (ASA), has served on the ASA’s Board, and has reviewed grants for organizations such as NIH, NCI, ACS, and NASA as both a standing member and ad hoc reviewer. In 2025, she will assume the role of the 120th President of the ASA.

Abstract: TBA

Banquet Talk : AI and Society – Opportunities and Challenges
Speaker: Dr. Eric Kolaczyk, McGill Computational and Data Systems Initiative (CDSI)
Eric Kolaczyk is a professor in the Department of Mathematics and Statistics, and inaugural director of the McGill Computational and Data Systems Initiative (CDSI). He is also an associate academic member of Mila, the Quebec AI Institute. His research is focused at the point of convergence where statistical and machine learning theory and methods support human endeavors enabled by computing and engineered systems, frequently from a network-based perspective of systems science. He collaborates regularly on problems in computational biology, computational neuroscience and, most recently, AI-assisted chemistry and materials science. He has published over 100 articles, including several books on the topic of network analysis. As an associate editor, he has served on the boards of JASA and JRSS-B in statistics, IEEE IP and TNSE in engineering, and SIMODS in mathematics. He formerly served as co-chair of the US National Academies of Sciences, Medicine, and Engineering Roundtable on Data Science Education. He is an elected fellow of the AAAS, ASA, and IMS, an elected senior member of the IEEE, and an elected member of the ISI.

Abstract: The interface where rapid AI development meets societal structures, mechanisms and norms is both filled with potential and fraught with concerns. Successfully navigating this interface arguably is one of the greatest challenges we face today. Statisticians and data scientists have a critical role to play in responding to this challenge, but the magnitude of our impact likely will be in direct proportion to the extent to which we engage deeply and simultaneously with multiple other fields. Such engagement is hard and requires us to rethink both our educational and research priorities. I will share some thoughts on these issues, drawing in part on my involvement setting up the new McGill Collaborative for AI and Society (McCAIS).