Talks – Principled Statistics in the Age of AI
Keynote Address: Prof. Xiao-Li Meng
Title: From a Cauchy Surprise to the Half-Cauchy Miracle
Abstract:
This talk follows the path from Pillai and Meng (2016, Annals of Statistics), “An unexpected encounter with Cauchy and Levy” to Liu, Meng, and Pillai (2025) “A Heavily Right Strategy for Integrating Dependent Studies in Any Dimension,” inviting the audiences to join a journey to explore an emerging and mystical force in principled statistical inference in arbitrarily high dimensions: heavy-tail approximations.
Bio:
Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files,” a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004-2012) and the Dean of Graduate School of Arts and Sciences (2012-2017).
Invited Talk: Prof. Cory McCartan
Title: TBD
Abstract:
TBD
Bio:
TBD
Invited Talk: Dr. Joshua Bon
Title: Persuasive Privacy
Abstract:
We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that are rigorously justified, while also allowing for the assessment of existing privacy guarantees through game theory. We show that pure and probabilistic differential privacy are special cases of our framework, and provide new interpretations of the post-processing inequality in this setting. Further, we demonstrate that privacy guarantees can be established for deterministic algorithms, which are overlooked by current privacy standards. (Joint work with James Bailie, Judith Rousseau, and Christian Robert.)
Bio:
Dr Bon is a Lecturer in the School of Mathematical Sciences at Adelaide University. His research is in computational statistics, focussing on the development and analysis of Bayesian inference algorithms, including sequential Monte Carlo and simulation-based inference. He collaborates with applied scientists across sports science, psychology, social science, and ecology to develop principled and robust data analysis procedures. Alongside this, he develops open-source statistical software. Dr Bon is currently working on methods for Bayesian inference with data privacy guarantees involving Persuasive Privacy.
Homepage: bonstats.github.io
Invited Talk: Prof. Fredrik Johansson
Title: Learning causally sound and interpretable composite endpoints for clinical trials
Abstract:
Randomized clinical trials are considered the gold standard evidence for learning about the causal effects of medical interventions, but have natural limitations on scope and length. This often rules out targeting long-term outcomes of interest, such as mortality or cardiovascular disease, as these endpoints won’t be observed for most participants during the length of the trial. Instead, researchers turn to surrogate endpoints that are associated with the primary outcome of interest and can be observed during the trial. This presents a problem: What constitutes a good surrogate? In theory, a good surrogate is one for which the effect of the treatment is predictive of its effect on the primary outcome, but the definition alone does not reveal how to find such a variable. More than that, to be useful in a clinical trial, the surrogate must be approved by a regulatory body when registering the trial, necessitating its interpretability. In this talk, I will discuss the implications of this, algorithms that can provably learn composite surrogates from observational data, and situations where there is no hope to find a good surrogate.
Bio:
Fredrik Johansson is associate professor of Computer Science & Engineering at Chalmers University of Technology, where he runs the Healthy AI Lab, dedicated to develop machine learning methods and theory to advance decision making in healthcare.
Homepage: healthyai.se
Invited Talk: Prof. Ashkan Panahi
Title: TBD
Abstract:
TBD
Bio:
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