STAI-X '26
Statistics and Trustworthy AI for
Cross (X)-Domain Acceleration

Early bird rate applies before June 1, 2026

To integrate statistics and artificial intelligence to accelerate trustworthy cross-domain discovery, innovation, and real-world impact.

Harvard University, Cambridge, MA • July 31 – August 1, 2026

Organized By StatsUpAI, an ASA Interest Group .

Latest Announcements

Data Challenge is Live May 4 – July 1. Learn more.
Registration is open: early bird by 6/1. Register here.
Information on Short Courses are posted. View here.
FAQ is here! Check it out.
Reviewer Nomination Form is now open. Submit here.
Intent to Submit Survey is now open. Submit here.
Apr 10 4-4:30pm EST: Info & Q&A Session with STAI-X 2026 organizers. Watch recording here.
OpenReview submission is now open. Paper track: Submit here. Poster track: Submit here.
Partner journals: JASA, AOAS, HDSR, ASA Discoveries, Canadian Journal of Statistics, Genome Research, GENETICS, Data Science in Science, and Statistics and Data Science in Imaging have joined us.
Partner organizations: NASEM-CATS, COPSS, NISS, ASA, ENAR, IMS, WNAR, ICSA, and SSC have joined us.

Topics

Foundations and Methods at the Interface of AI and Statistics

Foundation models, generative models and machine learning methods
Synthetic data
Statistical and theoretical foundations of AI
Statistical inference and generative AI

AI Agents and Benchmarks for Data-Driven Discovery

Agentic AI and co-scientists for X
Scalability for resource-constrained training environments
Benchmarking of AI-assisted tools and models

AI x Statistics x Science and Society

AI x statistics x health and biological science
AI x statistics x physical science and engineering
AI x statistics x social science, business, and law
Ethical AI

Why Publish in the STAI-X Proceedings

In a fast-moving field like AI, timely dissemination is critical. The STAI-X proceedings are designed to enable:

Rapid, High-Quality Peer Review

Reviews grounded in statistical x AI expertise.

Fast Dissemination

Cross-disciplinary research at the interface of statistics and AI and their real-world applications.

Increased Visibility & Impact

Early exposure of the work across statistics, AI, and domain science communities.

In addition, STAI-X collaborates with partner organizations and establishes various venues to promote accepted papers through their networks, amplifying reach and engagement across disciplines.