Section on Statistical Learning and Data Science to Hold Conference in New York

The ASA Section on Statistical Learning and Data Science will hold the 2026 SLDS Conference November 1–3 at the New York Marriott at the Brooklyn Bridge in New York City. Centered on the theme “Inference and Intelligence,” the conference will bring together researchers and practitioners from academia, industry, and government to exchange ideas at the forefront of statistical learning, data science, and artificial intelligence. The keynote program includes the following speakers:

• David Banks, Duke University, “The Future of Statistics”
• Dean Foster, Amazon, “Statistics and LLMs”
• David Rosenberg, Bloomberg, “Bond Price Nowcasting: Some Assembly Required”
• Bin Yu, University of California at Berkeley, “Veridical Deep Learning: Evaluation and Compositionality”
• Tian Zheng, Columbia University, “Statisticians in AI Education”

A total of 107 invited sessions over three days will cover a wide range of topics across statistical learning and data science. These sessions will reflect strong community engagement and the breadth of current work connecting statistical methodology, machine learning, artificial intelligence, and data-driven applications.

SLDS 2026 will also offer six half-day short courses designed to provide practical training on emerging tools and methodologies shaping the field, including the following:

• Statistical and Algorithmic Foundations of Diffusion Models, led by Yuxin Chen and Yuting Wei
• Beyond the ATE, led by Ivan Diaz, Kara Rudolf, and Nick Williams
• Statistical Foundations of Transfer Learning, led by Yang Feng
• Optimization for Statistics, led by George Michailidis
• Veridical Data Science in the Age of AI, led by Bin Yu, Tiffany Tang, and Chandan Singh
• Deep Learning Methods in Advanced Statistical Problems, led by Hongtu Zhu, Xiao Wang, and Runpeng Dai

Submissions to the Early Career Researcher Paper Award competition closed in May. Six winners will be selected, including at least three graduate students, based on peer review evaluating novelty, clarity, and rigor. Award winners will receive travel support and present in a dedicated session; other submissions will be merged into a poster session, with opportunities for poster awards. Questions about the competition may be directed to Nathaniel Sean O’Connell.

Support from the National Science Foundation will help facilitate participation by students and junior researchers. Additionally, a special issue in the Journal of Statistical Planning and Inference will be organized in connection with the conference. The issue will be guest-edited by Joshua Cape, Lucas Mentch, Yang Ning, and Boxiang Wang. Conference participants are invited to submit full papers for consideration, with submissions undergoing the journal’s standard peer-review process.

Visit the website to register and review the program, keynotes, short courses and conference logistics.