Conference Program

Sessions and Abstracts Program Book

At the Conference

Daily keynotes

Join us in Salon DE / Plenary for the keynote presentations:

• Sunday, November 1: Bin Yu, 9:00–10:00 a.m.; Tian Zheng, 5:30–6:30 p.m.
• Monday, November 2: Dean Foster, 9:00–10:00 a.m.; David Banks, 6:00–8:30 p.m. (banquet and keynote).
• Tuesday, November 3: David Rosenberg and Camilo Ortiz, 9:00–10:00 a.m.

Refreshments and evening events

• Breakfast and morning refreshments: 8:30–10:30 a.m. daily. Included with registration.
• Afternoon coffee break: 2:00–4:00 p.m. daily.
• Sunday welcome reception and poster session: November 1, 6:30–8:00 p.m., Salon F, G, H, and I. Included with conference registration.
• Monday banquet and keynote: November 2, 6:00–8:30 p.m., Salon DE / Plenary. Advance banquet registration is required. Collect your banquet ticket and drink voucher with your name badge.

See the full conference agenda for the daily schedule.

Timeline / Important Dates

• Monday, June 1, 2026: registration opens.
• Monday, August 31, 2026: early registration ends.
• Wednesday, September 30, 2026: abstract submission (and invited speaker registration) deadline.
• Monday, October 12, 2026: cancellation cut-off / housing deadline.
• Monday, October 12, 2026: Online registration closes at 11:59 p.m. EST because maximum capacity has been reached (updated).
• Wednesday, December 30, 2026: special issue submission deadline for the Journal of Statistical Planning and Inference.

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 particular, 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.

We gratefully acknowledge the funding support from the National Science Foundation and the local support from New York University, as well as sponsorships from corporations and institutions. See our Sponsors page for a full list of our supporters and sponsors.