Welcome to SLDS
2026: Inference and Intelligence
- Date/Time: November 1-3, 2026
- Location: Marriott at the Brooklyn Bridge, New York
City, NY
Timeline / Important Dates
- Friday, May 1, 2026: registration
opens.
- Tuesday, September 1, 2026: early
registration ends/abstract submission closes.
- Saturday, October 31, 2026: online
registration closes.
About SLDS Conference
The SLDS Conference is the flagship event of the
ASA
Section on Statistical Learning and Data Science (SLDS). Started in
2016 and scheduled biannually, it was interrupted by the COVID-19
pandemic but resumed in 2024.
The main goal of the conference is to bring together researchers in
statistical machine learning and artificial intelligence from academia,
industry, and government in a relaxed and stimulating atmosphere to
focus on the development of statistical learning theory, methods and
applications. In particluar, SLDS 2026 aims to:
- Disseminate recent advances at the intersection of statistics,
machine learning, and AI, with particular emphasis on principled
inference for modern data and intelligent systems.
- Create a highly interactive environment that accelerates
collaboration across theory, methodology, computation, and domain
applications.
- Broaden participation by supporting students, postdoctoral
researchers, and early-career investigators, including participants from
under-resourced institutions and groups historically underrepresented in
the field.
- Provide professional development opportunities for junior
researchers through technical training, mentoring, networking, and
leadership-building activities within the statistical and data science
community.
Topics include, but are not limited to high dimensional statistics,
big data analytics, deep learning, causal inference, graphical models,
learning theory, model selection, network analysis, text and image
analytics, spatiotemporal modeling and their applications in the health,
social and engineering sciences, and signal and image processing.
This conference is mainly sponsored by the ASA section on Statistical
Learning and Data Science. See our Sponsors
page for a full list of our sponsors.