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The Berkeley-Stanford Veridical Data Science Workshop is focused on showcasing and promoting veridical (truthful) data science (VDS) for reproducible, reliable data analysis and decision-making. It intends to build a community of veridical data science researchers for trustworthy data science, machine learning, and artificial intelligence. The discussions will promote opportunities for statisticians and data scientists to identify important VDS research topics and critical applications in academia and industry. Graduate students and early career researchers will benefit from this conference to find future research directions.

The one-day workshop will take place in person on Friday, May 31, 2024, at the UC Berkeley campus. (Virtual attendance is not available at this time.) 

• The program includes 4 keynotes, 10 invited speakers, and a lightning talk session.
• The cost is $25 (student discount available) for in-person attendance.
• Lunch will be provided.

This workshop is jointly hosted by:

• Berkeley Institute of Data Science (BIDS) and the Department of Statistics, parts of UC Berkeley's College of Computing, Data Science, and Society (CDSS)
• Stanford Data Science Center for Open and REproducible Science (SDS-CORES) 

The organizing committee includes Bin Yu (UC Berkeley, co-chair), Russ Poldrack (Stanford University, co-chair), Maya Mathur (Stanford University), and Tiffany Tang (University of Michigan).

More about VDS: A new book from MIT Press "Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making" is an essential source for producing trustworthy data-driven results, written by Bin Yu and Rebecca Barter.