Bin Yu selected as the 2026 Myles Hollander Distinguished Lecturer
The Department of Statistics at Florida State University has announced that Bin Yu, the Chancellor’s Distinguished Professor in the Departments of Statistics and Electrical Engineering and Computer Sciences at the University of California, Berkeley, is the 2026 Myles Hollander Distinguished Lecturer.
The lecture will take place at 2 p.m. ET on Friday, Nov. 6, 2026 in the Chemical Sciences Building Auditorium on the FSU main campus in Tallahassee. The live talk will also be accessible via Zoom. Register today.
Lecture Abstract
Data science underpins modern AI and many advances in health care, yet human judgment permeates every stage of the data science life cycle. These judgment calls introduce hidden uncertainties that go well beyond sampling variability and drive many of the risks associated with AI.
We introduce veridical data science, grounded in three fundamental principles —Predictability, Computability, and Stability (PCS) — to make such uncertainties explicit and assessable and to aggregate reality-checked algorithms for better results. The PCS framework unifies and extends best practices in statistics and machine learning and is illustrated through health care applications, including identifying genetic drivers of heart disease, reducing cost of prostate cancer detection, improving uncertainty quantification beyond standard conformal prediction, and proposing Green Shielding, a new user-centric framework for safeguarding users of AI.
About the Speaker
Dr. Yu earned a B.S. in mathematics from Peking University as well as M.S. and Ph.D. degrees in statistics from UC Berkeley. Prior to becoming Chancellor’s Distinguished Professor, she was a member of technical staff at Lucent Bell Labs, a distinguished researcher in the Deep Learning Group at Microsoft Research, an assistant professor at the University of Wisconsin-Madison, and Miller Research Professor at UC Berkeley. She also serves as core faculty at the Center for Computational Biology and senior advisor at the Simons Institute for the Theory of Computing.
Dr. Yu’s current research focuses on interpretable machine learning, causal inference, and the development of stable, reproducible methods for scientific discovery. Throughout her career, she has made fundamental contributions to statistical theory, including pioneering work on VC theory for time series analysis, minimum description length and entropy estimation, sparse modeling, boosting, spectral clustering, and MCMC convergence analysis. Her interdisciplinary applied work spans neuroscience, genomics, remote sensing, and precision medicine.
Dr. Yu is a member of the U.S. National Academy of Sciences and the American Academy of Arts and Sciences. She was a Guggenheim Fellow and a Chan Zuckerberg Biohub Investigator. She has delivered several distinguished lectures, including the Breiman Lectures, the COPSS Distinguished Achievement Award and Lecture, the Tukey Memorial Lecture, and the Wald Memorial Lecture.