Fri, Feb 20, 2026, 09:00 – 10:00AM (PDT), 12:00 – 13:00PM (EST)
Title: Veridical Data Science towards Trustworthy AI
Abstract:
What does it take to build AI systems we can genuinely trust? In this talk, I'll introduce the PCS framework—Predictability, Computability, and Stability—a practical approach to veridical (truthful) data science that ensures reliable and actionable insights.
I'll share success stories from healthcare, showing how PCS principles have improved cancer detection and found genetic drivers of a heart disease, while reducing costs. A key challenge is uncertainty quantification: teaching AI to communicate how confident we should be in its predictions. I'll introduce PCS-UQ, which testing across 26 datasets shows outperforms standard methods, delivering tighter confidence intervals with better coverage across different subgroups—essential for ensuring AI works fairly for everyone.
We'll conclude with actionable steps for making statistical modeling more trustworthy, along with resources to apply PCS principles in your own work. Whether you're building models or making decisions based on AI, you'll gain concrete insights into creating data science that delivers on its promises.
Speaker: Dr. Bin Yu
Bin Yu is CDSS Chancellor's Distinguished Professor in Statistics, EECS, Center for Computational Biology, and Senior Advisor at the Simons Institute for the Theory of Computing, all at UC Berkeley. Her research focuses on the practice and theory of statistical machine learning, veridical data science, responsible and safe AI, and solving interdisciplinary data problems in neuroscience, genomics, and precision medicine. She and her team have developed algorithms such as iterative random forests (iRF), stability-driven NMF, adaptive wavelet distillation (AWD), Contextual Decomposition for Transformers (CD-T), SPEX and ProxySPEX for interpreting deep learning models, especially for compositional interpretability.
She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow, President of Institute of Mathematical Statistics (IMS), and delivered the Tukey Lecture of the Bernoulli Society, the Breiman Lecture at NeurIPS, the IMS Rietz Lecture, and the Wald Memorial Lectures (the highest honor of IMS), and Distinguished Achievement Award and Lecture (formerly Fisher Lecture) of COPSS (Committee of Presidents of Statistical Societies). She holds an Honorary Doctorate from The University of Lausanne. She is on the Editorial Board of Proceedings of National Academy of Science (PNAS) and a co-editor of the Harvard Data Science Review (HDSR).
