[Lilab] Fwd: Webinar Invitation: NISS Ai, Statistics & Data Science in Practice "Recent Advances in the Statistical Foundations of Large Language Models"
Wei Vivian Li
weil at ucr.edu
Thu Mar 12 11:39:54 PDT 2026
FYI.
Also a reminder that we will review the literature summary today.
---------- Forwarded message ---------
From: NISS Communications <communications at niss.org>
Date: Thu, Mar 12, 2026 at 6:30 AM
Subject: Webinar Invitation: NISS Ai, Statistics & Data Science in Practice
"Recent Advances in the Statistical Foundations of Large Language Models"
To: Vivian <weil at ucr.edu>
Join us for our next webinar featuring Weijie Su, Wharton Statistics & Data
Science at UPenn; Moderator: Whitney Huang, Statistics at Clemson
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Join us for our next Ai, Statistics & Data Science in Practice webinar:
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NISS AI, Statistics & Data Science in Practice Webinar: Recent Advances in
the Statistical Foundations of Large Language Models*Date: Tuesday, March
17, 2026 - 12:00pm to 1:30pm ET**Abstract*
In this talk, we advocate for the development of rigorous statistical
foundations for large language models (LLMs). We begin by elaborating two
key features that motivate statistical perspectives for LLMs: (1) the
probabilistic, autoregressive nature of next-token prediction, and (2) the
complexity and black box nature of Transformer architectures. To illustrate
how statistical insights can directly benefit LLM development and
applications, we present two concrete examples. First, we introduce a novel
statistical framework to analyze the efficiency of watermarking schemes,
with a focus on a watermarking scheme developed by OpenAI for which we
derive optimal detection rules that outperform existing ones. Second, we
demonstrate statistical inconsistencies and biases arising from the current
approach to aligning LLMs with human preference. We propose a
regularization term for aligning LLMs that is both necessary and sufficient
to ensure consistent alignment. Collectively, these findings showcase how
statistical insights can address pressing challenges in LLMs while
simultaneously illuminating new research avenues for the broader
statistical community to advance responsible generative AI research. This
talk is based on arXiv:2404.01245, 2405.16455, 2503.10990, and 2510.22007.
*Speaker*
*Weijie Su, *Associate Professor, Wharton Statistics and Data Science
Department and, by courtesy, Department of Biostatistics, Epidemiology,
and Informatics, University of Pennsylvania
*Moderator*
*Whitney Huang*, Associate Professor of Statistics at Clemson University
*See full details on event page:*
NISS AI, Statistics & Data Science in Practice Webinar: Recent Advances in
the Statistical Foundations of Large Language Models | National Institute
of Statistical Sciences
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Register on Zoom
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About the Speaker
Weijie Su
*Weijie Su *is an Associate Professor in the Wharton Statistics and Data
Science Department and, by courtesy, in the Department of Biostatistics,
Epidemiology, and Informatics at the University of Pennsylvania. He serves
as a Co-Director of the Penn Research in Machine Learning Center. He
received his Ph.D. in Statistics from Stanford University in 2016 and his
Bachelor's degree in Mathematics from Peking University in 2011. His
research interests span the statistical foundations of generative AI,
high-dimensional statistics, privacy-preserving machine learning, and
optimization.
He is a founding Co-Editor of the journal Statistical Learning and Data
Science and serves as an Associate Editor for JASA, AOAS, OPRE, JMLR, FnT
in Statistics, and Harvard Data Science Review. He currently serves on the
Organizing Committee of ICML 2026 as Scientific Integrity Chair, where his
isotonic mechanism will be deployed to enhance peer review. His work has
been recognized with many honors, including the Stanford Theodore Anderson
Dissertation Award, NSF CAREER Award, Sloan Research Fellowship, IMS Peter
Hall Prize, SIAM Early Career Prize in Data Science, ASA Noether Early
Career Award, ICBS Frontiers of Science Award in Mathematics, and IMS
Medallion Lectureship. He has authored two discussion papers in JRSSB and
JASA and is a Fellow of the IMS. See Profile
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Meet the Moderator
Whitney Huang
*Whitney Huang *is an Associate Professor of Statistics at Clemson
University, where he has served since August 2019. Prior to joining
Clemson, he was a Canadian Statistical Sciences Institute (CANSSI) and
Statistical and Applied Mathematical Sciences Institute (SAMSI)
postdoctoral fellow at the University of Victoria (UVic), affiliated with
the Pacific Climate Impacts Consortium and the School of Earth and Ocean
Sciences, working with Dr. Francis Zwiers and Prof. Adam Monahan. Before
his time at UVic, he held a SAMSI/University of North Carolina postdoctoral
position under the supervision of Prof. Richard Smith.
He received his Ph.D. in Statistics from Purdue University in August 2017,
advised by Prof. Hao Zhang. During his doctoral studies, he was actively
involved in the Research Network for Statistical Methods for Atmospheric
and Oceanic Sciences (STATMOS) and the Center for Robust Decision Making on
Climate and Energy Policy (RDCEP), collaborating with Michael Stein and
Elisabeth Moyer at the University of Chicago and Doug Nychka at the
National Center for Atmospheric Research. Before pursuing his doctorate at
Purdue, he earned a Master’s degree in Statistics from the University of
Akron and a Bachelor’s degree in Mechanical Engineering from National Cheng
Kung University in Taiwan. His research interests include statistics of
extremes, spatio-temporal statistics, surrogate modeling for computer
experiments, time-frequency analysis, multiscale statistical modeling,
spatial point processes, environmental applications, and high-frequency
physiological data analysis. See Profile
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About the Ai, Statistics & Data Science in Practice Webinar Series
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NISS AI, Statistics and Data Science in Practice is a monthly webinar
series that brings together leading experts from industry and academia to
discuss the latest advances and practical applications in AI, data science,
and statistics. Each session features a keynote presentation on
cutting-edge topics, where attendees can engage with speakers on the
challenges and opportunities in applying these technologies in real-world
scenarios. This series is intended for professionals, researchers, and
students interested in the intersection of AI, data science, and
statistics, offering insights into how these fields are shaping various
industries. The series is designed to provide participants with exposure to
and understanding of how modern data analytic methods are being applied in
real-world scenarios across various industries, offering both theoretical
insights, practical examples, and discussion of issues.
During Spring 2026, from January through May 2026, the series will focus on
large language models (LLMs) and the statistical and methodological
foundations required to develop, evaluate, and deploy them responsibly and
effectively. As LLMs become central to a wide range of scientific,
industrial, and societal applications, careful attention to data
generation, model training, evaluation, and inference is essential to
ensure reliability, robustness, and transparency. As LLMs become
increasingly central to scientific research, industry workflows, and
societal decision-making, rigorous attention to how training data are
constructed, curated, and sampled is critical for understanding model
behavior and limitations. The series will highlight methodological
considerations in model training and fine-tuning, including sources of
bias, variability, and uncertainty, as well as principled approaches to
benchmarking and evaluation that move beyond surface-level performance
metrics. Emphasis will be placed on transparent and reproducible evaluation
frameworks that support meaningful comparisons across models and use cases,
and on statistical perspectives that help clarify what LLM outputs do and
do not represent. By grounding discussions of LLM development and
deployment in sound statistical reasoning, the series aims to promote more
reliable, interpretable, and trustworthy language models in practice.
*Upcoming Webinars in Series*
-
Recent Advances in the Statistical Foundations of Large Language Models
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- Speaker: *Weijie* *Su*, March 17, 2026
-
Ai, Statistics & Data Science in Practice Webinar - April 17, 2026
<https://niss.us11.list-manage.com/track/click?u=3140364b3158a2b6dfdaa8cbe&id=81b3ee9cdd&e=74727804bb>
- Speaker: *Anastasios N Angelopoulos*, April 17, 2026
-
Causal Generalist Medical AI
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- Speaker: *Hongtu Zhu*, May 19, 2026
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NISS AI, Statistics & Data Science in Practice Webinar: Recent Advances in
the Statistical Foundations of Large Language Models
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Event Date:
* Tuesday, March 17, 2026 - 12:00pm to 1:30pm ET*
Event Type:
NISS Hosted
Event Location:
Free Zoom Webinar
Speaker: Weijie Su, Associate Professor, Wharton Statistics and Data
Science Department and, by courtesy, Department of Biostatistics,
Epidemiology, and Informatics, University of Pennsylvania Moderator Whitney
Huang, Associate Professor of Statistics at Clemson University Zoom
Registration... more
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NISS-Merck Meet-Up: Transforming Statistics & Drug Development: The Role of
Large Language Models
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Event Date:
* Wednesday, March 25, 2026 - 12:00pm to 1:30pm ET*
Event Type:
NISS Hosted
Event Location:
Free Zoom Webinar
Overview Join us for the NISS/Merck Meet-Up: Transforming Statistics and
Drug Development: The Role of Generative AI and Large Language Models on
March 25, 2026. This event will bring together experts from academia,
industry, and government to explore how cutting-edge AI
technologies—particularly... more
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DFW Data Science and Statistics Day 2026
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Event Date:
* Thursday, March 26, 2026 - 4:00pm-6:00pm CT*
Event Type:
NISS Sponsored
The D³ (DFW Data Science and Statistics Day) poster competition provides an
excellent platform for students and postdocs in data science,
statistics/biostatistics, and business in the DFW area to present their
research and connect with peers. This event welcomes both statisticians and
applied... more
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COPSS-NISS Leadership Webinar: Leadership in Translational Statistics and
Machine Learning: Working Hand-in-Hand with the Clinical World
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Event Date:
* Tuesday, March 31, 2026 - 12:00pm to 1:00pm ET*
Event Type:
NISS Hosted, NISS Sponsored
Event Location:
Free Zoom Webinar
Overview Full details coming soon! Register on Zoom Panelists Dr. Maya
Petersen, Professor of Biostatistics, Epidemiology and Computational
Precision Health at University of California, Berkeley Dr. Jenna Wiens,
Associate Professor of Computer Science and Engineering at University of...
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NISS Short Course Series: Foundations of Time Series Analysis with R with
Tucker S. McElroy
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Event Type:
NISS Hosted
Event Location:
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Overview: Register for this twelve‑week short course series led by Tucker
S. McElroy, Senior Time Series Mathematical Statistician at the U.S. Census
Bureau! Across twelve one‑hour sessions, participants will walk through the
full content of Time Series: A First Course with Bootstrap Starter, with...
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NISS-CANSSI Collaborative Data Science Webinar: Opportunities for
Statisticians in AI Education
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Event Date:
* Thursday, April 9, 2026 - 1:00pm to 2:00pm ET*
Event Type:
NISS Hosted
Event Location:
Free Zoom Webinar
Overview: Join us for our upcoming webinar exploring emerging opportunities
for statisticians in AI education! Hear perspectives from statisticians who
are actively involved in AI education initiatives, highlighting concrete
examples, challenges, and best practices. Register on Zoom Speakers... more
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UGA ASA DataFest 2026
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Event Date:
* Friday, April 10 - Sunday, April 12 2026, All day*
Event Type:
NISS Sponsored
Event Location:
UGA Innovation District, 210 Spring Street, Athens, GA, 30602
United States
Overview: The Department of Statistics is excited to host UGA’s first-ever
ASA DataFest, an engaging and fast-paced, weekend-long data analysis
competition sponsored by the American Statistical Association (ASA). Over
the course of 48 hours, undergraduate and graduate students will work in...
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COPSS-NISS Leadership Webinar: Leadership in Esametry: Within-Individual
Statistics of N-of-1 Trials and Single-Case Designs
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Event Date:
* Tuesday, April 14, 2026 ET 2:30 pm - 3:30 pm ET*
Event Type:
NISS Hosted, NISS Sponsored
Event Location:
Free Zoom Webinar
Overview: Join us for our next COPSS-NISS Leadership Webinar: Leadership in
Esametry: Within-Individual Statistics of N-of-1 Trials and Single-Case
Designs. Over many decades, various aspects of quantitative idiography have
been developed in a variety of fields—with different areas of
application,... more
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Ai, Statistics & Data Science in Practice Webinar - April 17, 2026
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Event Date:
* Tuesday, April 17, 2026 - 12:00pm to 1:30pm ET*
Event Type:
NISS Hosted
Event Location:
Free Zoom Webinar
Speaker: Anastasios N Angelopoulos, UC Berkeley Moderator Coming Soon
Zoom Registration Coming Soon! Overview Coming Soon About the Speaker
Anastasios Nikolas Angelopoulos is a sixth-year Ph.D. student at the
University of California, Berkeley, advised by Michael I. Jordan and
Jitendra... more
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AI Day for Federal Statistics 2026
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Event Date:
*Thursday, April 30, 2026 - 1:00pm to 5:00pm*
Event Type:
NISS Co-Hosted, NISS Sponsored
Event Location:
National Academy of Sciences Building
2101 Constitution Ave NW
Washington, District of Columbia, 20418
United States
Overview: Ready or not, Generative Artificial Intelligence (Gen AI) is
here. The second AI Day for Federal Statistics workshop will be held April
30th, 2026, from 1-5 pm ET. The Committee on National Statistics (CNSTAT),
the Federal Committee on Statistical Methodology (FCSM), and the
National... more
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NISS-CANSSI Collaborative Data Science Webinar: Bayesian Reconstruction of
Ion Temperature and Amplitude Profiles in Inertial Confinement Fusion
Diagnostics
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Event Date:
* Friday, May 8, 2026 - 1:00pm to 2:00pm ET*
Event Type:
NISS Hosted
Event Location:
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Abstract: Inertial confinement fusion (ICF) experiments rely on accurate
ion temperature and emission measurements to diagnose plasma conditions and
improve performance. However, due to technical challenges and limited
signal, existing ion temperature diagnostics lack spatial resolution,...
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NISS Ai, Statistics and Data Science in Practice Webinar: "Causal
Generalist Medical AI" with Dr. Hongtu Zhu
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Event Date:
* Tuesday, May 19, 2026 - 12:00pm to 1:30pm ET*
Event Type:
NISS Hosted
Event Location:
Free Zoom Webinar
Speaker: Dr. Hongtu Zhu, Kenan Distinguished Professor of Biostatistics,
Statistics, Radiology, Computer Science and Genetics at the University of
North Carolina at Chapel Hill Moderator Dr. Hongyuan Cao, Associate
Professor in the Department of Statistics at Florida State University
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