Just a reminder that SDSS abstract submissions are due on December 10. If you're considering submitting a session, or contributed talk/poster, now's a great time to get your abstract in. Please feel free to share this deadline with colleagues and students who might be interested, especially those working at the intersection of statistics, data science, and practice.
Will you be among the innovators contributing to the 2026 Symposium on Data Science & Statistics program? Artificial intelligence is redefining what's possible in data science and statistics, but progress depends on experts like you. Submit an abstract to present at SDSS 2026 and join peers who are shaping how AI, statistics, and data science advance together.
All refereed submissions will undergo a blind review process conducted by members of the SDSS Program Committee. The top-ranked abstracts will be selected for presentation at the symposium. SDSS 2026 will be held April 28 – May 1 in Milwaukee, Wisconsin.
Consider contributing to a program that will deliver knowledge participants can apply to advance their work. Learn more and submit by December 10.
2026 Symposium on Data Science and Statistics
This year’s program features two distinguished plenary speakers:
- Lorin Crawford, a Principal Researcher at Microsoft Research. His work focuses on developing interpretable machine learning and AI algorithms to explore how genetic effects and gene-by-environment interactions influence complex traits and disease progression. Dr. Crawford has been recognized on the Forbes 30 Under 30 list and The Root 100 Most Influential African Americans.
- Jeff Morris, the George S. Pepper Professor of Public Health and Preventive Medicine and the Director of the Division of Biostatistics at the University of Pennsylvania. An IMS and ASA Fellow, Dr. Morris specializes in quantitative methods for extracting knowledge from biomedical big data. He is also widely recognized for his science communication efforts across social and traditional media.
The symposium will also feature a plenary panel discussion titled "Statistical Thinking: A Critical Piece in the Age of AI". This session will be moderated by Karl Pazdernik (Chief Data Scientist & Team Lead, AI & Data Analytics, Pacific Northwest National Laboratory). He will be joined by panelists Frank Alexander (Director for AI Strategy and Research, Argonne National Laboratory) and Rui (Sammi) Tang (SVP and Global Head of Quantitative Sciences and Evidence Generation, Astellas Pharmaceuticals).
Attendees can also participate in the following short courses for a variety of skills and interests :
- Modern Machine Learning with Bayesian Additive Regression Trees, led by Robert E McCulloch of Arizona State University and Rodney Sparapani of Medical College of Wisconsin
- Getting Started with Positron: A Next-Generation IDE for Data Science, led by Mine Çetinkaya-Rundel of Duke University
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Expanding the Statistician’s Toolkit: Building and Sharing Data Science Tools in R, led by Mehdi Maadooliat, Jaihee Choi, and Daniel Cirkovic of Marquette University
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Towards Trustworthy Statistical Inference with Black-box AI Predictions, led by Jiwei Zhao of University of Wisconsin–Madison
- Everyday Reproducibility, led by Gregory Hunt of William & Mary and Johann Gagnon-Bartsch of University of Michigan
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Praveen Gupta Sanka
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