StatsUpAI

2025 Annual Newsletter

Empowering statisticians to participate in AI research and leadership

A year of milestones, innovation, and community growth

Tian Zheng

A Message from the Chair

December 2025

As we close out 2025, I want to take a moment to reflect on the mission of StatsUpAI — and what the name means to convey. It is an invitation to action: let's elevate the role of statistics in shaping the future of artificial intelligence. It's a nod to rigor, and to a vision of AI that is principled, transparent, and grounded in data-driven understanding.

This year has been a remarkable one as you will learn from reading this newsletter. Each of these accomplishments reflects not just what we've built, but who has built it — a dedicated, generous, and vibrant network of researchers, students, educators, and practitioners.

To all of you: thank you for being part of the StatsUpAI community. Your engagement, curiosity, and collaboration have been the foundation of everything we've achieved.

And to the volunteers who have worked behind the scenes — building our website and social media contents, curating resources, and handling countless logistics — your quiet leadership has powered this momentum. We truly could not have done this without you.

Looking ahead to 2026, we're excited to continue building, learning, and growing — together.

Warmest wishes for the new year,

Tian Zheng

Chair, StatsUpAI

Columbia University

2025 By The Numbers

ASA
Interest Group
12
Seminars
5
Interviews
5
Short Courses
100+
Resources
700+
Webinar Participants
120+
ASA Members
370+
Google Group Subscribers

The rapid expansion of artificial intelligence has fundamentally reshaped how data are generated, analyzed, and interpreted across scientific disciplines. In this evolving landscape, the role of statistics has never been more critical—not only to improve model performance, but to ensure reliability, interpretability, and principled decision-making in AI-driven research.

Against this backdrop, 2025 marked a defining year for StatsUpAI. Recognized as an official Interest Group by the American Statistical Association (ASA), StatsUpAI has emerged as a growing community for advancing statistically grounded AI research. This milestone reflects a shared commitment within our community to place statistical rigor, uncertainty quantification, and methodological transparency at the core of modern AI development.

In 2025, our theme was AI in health data science. StatsUpAI highlighted research and collaboration spanning neuroimaging, genomics, electronic health records, and clinical trials. By highlighting statisticians who are leaders in these interdisciplinary fields, StatsUpAI promotes approaches that are not only powerful, but also trustworthy, reproducible, and clinically meaningful—demonstrating how statistics continues to shape the future of health data science in the AI era.

ASA Community Events

2025 marked a major milestone for StatsUpAI within the American Statistical Association (ASA).

Major Milestone

StatsUpAI Officially Becomes an ASA Interest Group

On January 30, 2025, StatsUpAI was formally approved as an Interest Group of the American Statistical Association (ASA). This milestone marks StatsUpAI's integration into the ASA community as a dedicated forum for advancing statistical foundations, methodological rigor, and uncertainty-aware AI research—particularly in health and biomedical data science.

Steering Committee

The Steering Committee brings together leading statisticians and data scientists across institutions to guide the mission, activities, and scientific direction of StatsUpAI within the ASA community.

David Banks (Duke)
Edgar Dobriban (UPenn)
Jian Kang (Michigan)
Xihong Lin (Harvard)
Daniel Nettleton (Iowa State)
Wenyi Wang (MD Anderson)
Tian Zheng (Columbia)
Hongtu Zhu (UNC)

Inaugural Officers

C
Chair: Tian Zheng
Columbia University
P
Program Chair: Edgar Dobriban
University of Pennsylvania
O
Outreach Chair: Wenyi Wang
MD Anderson Cancer Center
S
Secretary: Jian Kang
University of Michigan
Important Conference

2025 ISU-NISS Conference on AI and Statistics

A three-day conference co-hosted with Iowa State University and the National Institute of Statistical Sciences, bringing together researchers at the intersection of AI and statistical science.

Date: September 12-14, 2025
Location: Iowa State University
View Conference Details
Community Event

Stats Up AI Business Meeting @ JSM 2025

A business meeting held during JSM 2025, including Steering Committee updates, discussion of future initiatives, and networking.

Date: August 4, 2025
Time: 4:30 PM - 6:30 PM
Location: Music City Center, CC-101D

Seminar Overview

In 2025, StatsUpAI organized twelve online seminars focused on statistical and AI methods for health data science, with a particular emphasis on neuroimaging and the analysis of fMRI and EEG data. The seminar series attracted strong engagement from the community, with the inaugural session drawing over 120 participants.

The Statistical and AI Methods for Health Data Science series featured speakers from leading institutions, including Johns Hopkins University, Emory University, KAUST, and Stanford University. Topics spanned functional neuroimaging, reproducible neuroscience, and data-driven methodological advances, fostering interdisciplinary exchange across statistics, AI, and biomedical research.

Topic Distribution

Interviews with Leaders Series

In 2025, StatsUpAI conducted a series of in-depth interviews with leading scholars and research teams, covering emerging topics such as tumor heterogeneity analysis, cross-disciplinary research in statistics and AI, and biomedical knowledge graphs.

These interviews highlighted recent methodological and scientific advances while providing the community with first-hand perspectives on current research frontiers and future directions in data-driven biomedical science.

Short Courses

In 2025, StatsUpAI organized and supported high-quality short courses covering cutting-edge topics in statistics, AI, and their applications. These courses provide researchers and practitioners with opportunities to deepen their understanding of modern methodologies and their real-world applications.

Short Course

Decision Intelligence for Two-sided Marketplaces

An in-depth tutorial on decision intelligence in two-sided marketplaces, emphasizing reinforcement learning and sequential decision-making for long-term optimization.

Course Topics

Fundamentals of Two-sided Marketplaces (35 min)
Policy Optimization for Strategic Decisions (75 min)
A/B Testing: Policy Evaluation and Experimental Design (75 min)
Optimizing Markets with LLMs and Digital Twins (20 min)

Organizers

Zhiwei (Tony) Qin, Chengchun Shi, Hongtu Zhu

Visit Course Website
Short Course

BIOS 740: Deep Learning Methods in Biomedical Sciences with PyTorch

A comprehensive course exploring modern deep learning architectures—including CNNs, RNNs, GANs, and Transformers—and their applications to challenging statistical problems in biomedical sciences.

Course Highlights

Deep Learning Architectures: CNNs, RNNs, GANs, Transformers
PyTorch Implementation and Hands-on Coding
Applications to Biomedical and Statistical Problems

Course Information

Spring 2026 Offering • UNC Biostatistics

Visit Course Website
Short Course

Duke & Chen Institute Joint AI Boot Camp 2025

A five-day intensive program exploring cutting-edge applications of AI in healthcare and medical research, featuring distinguished faculty and cutting-edge presentations.

Key Topics

Causal Generalist Medical AI (CGM-AI)
Real-World Evidence & Clinical Trials
Scalable NLP for Healthcare
Foundation Models for Genomics

Event Details

May 12-16, 2025 • Duke University

Hosted by Prof. Jian Pei

View Full Details
Short Course

JCSDS 2025: Large Language Models and AI

An introduction to large language models and their applications in statistics and data science, covering foundational concepts and practical implementations.

Instructor

Linjun Zhang, Rutgers University

Short Course

JSM 2025: Large Language Models for Statisticians and Data Scientists: Opportunities and Research Topics

Exploring opportunities and research topics in applying large language models to statistical and data science challenges.

Instructor

Linjun Zhang, Rutgers University

Core Resources Statistics

StatsUpAI.org continuously provides high-quality open-source resources to the community. Our resource library encompasses review articles, dataset information resources, and processing pipelines, offering researchers comprehensive support from theory to practice.

Over the past year, we have continuously expanded our resource library, adding multiple review articles covering cancer research, electronic health records, genetics, medical imaging, and other fields. We have also curated information on 83 datasets across 9 major categories and 4 standardized processing pipelines, providing researchers with access to dataset metadata and resources for health data science research.

Review Articles

0

Total Review Articles

View All Articles

Dataset Resources

0

Dataset Information Resources

Browse Dataset Resources

Pipeline

0

Processing Pipeline Categories

View All Pipelines

Looking Forward

Reflecting on 2025, StatsUpAI made substantial progress as a growing academic community. Key milestones—including recognition as an ASA Interest Group, the organization of academic events, and the development of shared resources—underscore our continued efforts to advance statistically grounded AI research.

Looking ahead, we will continue to focus on:

Expanding Community Impact

Connecting researchers and practitioners worldwide through more seminars, conferences, and training workshops, fostering interdisciplinary collaboration and knowledge sharing.

Enriching Resource Library

Continuously updating and expanding review articles, datasets, and processing pipelines to provide the community with more comprehensive and cutting-edge research resources.

Advancing Innovative Research

Supporting cutting-edge research at the intersection of statistical methods, AI technologies, and emerging applications, driving theoretical breakthroughs and practical impact.

Cultivating the Next Generation

Nurturing the next generation of statisticians and data scientists through educational programs and mentorship initiatives, passing on professional knowledge and innovative spirit.

Thank you to all community members for your support and participation. Let us look forward to even greater achievements for StatsUpAI in 2026!

Stats Up AI Communication Team

Chen, KevinDeBakey High School
Dobriban, EdgarUniversity of Pennsylvania
Gao, ShanYunnan University, UNC-Chapel Hill
Geng, YuhanUniversity of Michigan
Kang, JianUniversity of Michigan
Liu, XiaoqianUC Riverside
Li, RuonanMD Anderson Cancer Center
Li, YulinRutgers University
Qu, AnnieUC Irvine
Quazi, MohammedWest Virginia University
Wang, WenyiMD Anderson Cancer Center
Ye, HanwenUC Irvine
Zhao, YiIndiana University
Zhao, BangyaoUniversity of Michigan
Zhang, PanpanVanderbilt University
Zhang, LinjunRutgers University
Zheng, TianColumbia University
Zhu, HongtuUNC Chapel Hill

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