Keynote Presentation Ⅰ : Neural Adversarial Causal AI: Learning from Heterogeneous 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: This talk develops nonparametric invariance and causal learning from multiple environments regression models in which data from heterogeneous 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 or quasi-causal variables is invariant across environments. Our idea of invariance and causal learning is to find a set of variables as exogenous as possible across multiple environments to minimize the empirical loss. To realize this idea, we proposed a Neural Adversial Invariant Learning (NAIL) frame, in which the unknown regression is represented by a Relu network, and invariance across multiple environments is tested using adversarial networks. Leveraging the representation power of neural networks, we introduce neural causal networks based on a focus adversarial invariance regularization (FAIR) and its novel training algorithm. It is shown that one can find the invariant variables and quasi-invariant variables and that the resulting procedure is adaptive to low-dimensional composition structures. The procedures are convincingly demonstrated using simulated examples. (Joint work with Cong Fang, Yihong Gu, and Peter Buelhmann)

Keynote Panel Discussion Ⅰ
Panelist: Dr. Dan Nettleton, Iowa State University
Dan Nettleton is Laurence H. Baker Endowed Chair of Biological Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. Since 2019, Nettleton has served as chair of the Iowa State Department of Statistics, one of the first and largest departments of statistics in the United States. Nettleton’s research interests include statistical methods for the design and analysis of high-dimensional biological datasets and the development statistical learning methodology for predictive inference. Nettleton’s research has been supported by funding from government agencies (including NIH, NSF, and USDA) for the past 25 years.

Nettleton currently serves as chair of the American Statistical Association Caucus of Academic Representatives, which consists of chairs and heads of departments of statistics and biostatistics in the United States. In 2024, he completed a four-year term as secretary of the American Association for the Advancement of Science Section U (Statistics). Honors include Fellow of the American Statistical Association, the Iowa State College of Liberal Arts and Sciences Awards for Early Achievement in Departmental Leadership and Outstanding Achievement in Departmental Leadership, the Iowa State University Margaret Ellen White Graduate Faculty Award for excellent guidance and encouragement of graduate students, and the Iowa State College of Liberal Arts and Sciences Award for Outstanding Career Achievement in Research.

Panelist: Dr. Faris Sbahi, Normal Computing
Faris is the CEO and co-founder of Normal Computing. He is a former Google Brain and Google X engineer, where he pioneered probabilistic AI for some of the largest decision-making problems in the world.

Moderator: Dr. Yuchen Fama, Normal Computing
Yuchen is the Chief Product Officer of Normal Computing and Elected Council Member of NESS. She has been working in the field of ML/AI for over a decade and is passionate about turning research and technology breakthroughs into useful products for the world. She obtained her Ph.D. in Statistics at UConn in 2010.

Keynote Presentation Ⅱ : Introducing the Forster-Warmuth Nonparametric Counterfactual Regression
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: Series or orthogonal basis regression is one of the most popular non-parametric regression techniques in practice, obtained by regressing the response on features generated by evaluating the basis functions at observed covariate values. The most routinely used series estimator is based on ordinary least squares fitting, which is known to be minimax rate optimal in various settings, albeit under stringent restrictions on the basis functions and the distribution of covariates. In this work, inspired by the recently developed Forster-Warmuth (FW) learner, we propose an alternative series regression estimator that can attain the minimax estimation rate under strictly weaker conditions imposed on the basis functions and the joint law of covariates, than existing series estimators in the literature. Moreover, a key contribution of this work generalizes the FW-learner to a so-called counterfactual regression problem, in which the response variable of interest may not be directly observed (hence, the name ``counterfactual'') on all sampled units, and therefore needs to be inferred to identify and estimate the regression in view from the observed data. Although counterfactual regression is not entirely a new area of inquiry, we propose the first-ever systematic study of this challenging problem from a unified pseudo-outcome perspective. In fact, we provide what appears to be the first generic and constructive approach for generating the pseudo-outcome (to substitute for the unobserved response) which leads to the estimation of the counterfactual regression curve of interest with small bias, namely bias of second order. Several applications are used to illustrate the resulting FW-learner including many nonparametric regression problems in missing data and causal inference literature, for which we establish high-level conditions for minimax rate optimality of the proposed FW-learner.

This is joint work with Yachong Yang and Arun kuchibhotla.

Keynote Presentation Ⅲ : Everyday Statistician's Impact: Advancing Science in Team Environments
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: In a world that often celebrates exceptionalism, the significant contributions of everyday individuals are frequently overlooked. Terms like 'exceptional' and 'outstanding,' often emphasized in grant critiques, may overshadow the vital roles played by the majority. Inspired by a scene in the movie Barbie, where the voice of an ordinary person makes a profound impact, I explore the invaluable yet frequently invisible contributions of everyday statisticians. As an everyday biostatistician, collaborator, and team scientist, I will share personal anecdotes and professional insights demonstrating how statistical thinking and collaborative leadership significantly advance science and improve patient care. Furthermore, as the incoming 2025 President of the American Statistical Association, I will outline my strategic initiatives aimed at building strong bridges within the ASA community and beyond. These initiatives emphasize the essential roles of all 'everyday' professionals and highlight the importance of diverse perspectives and the appreciation of data and statistics in this new AI era.

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).