Foundations of Stable, Generalizable and Transferable Statistical Learning

Mathematical Sciences Research Institute (MSRI)


March 7, 2022 to March 10, 2022
8:00am to 12:15pm
Virtual Participation


BIDS Faculty Affiliate Bin Yu is an organizer of this upcoming workshop on the Foundations of Stable, Generalizable and Transferable Statistical Learning, being hosted by the Mathematical Sciences Research Institute (MSRI) on March 7-10, 2022. This workshop will be held virtually (online via Zoom) from 8:00 AM - 12:15 PM US Pacific Time (UTC -8) each day, including a 30 minute lunch/dinner break.

Register: Pre-registration is required. MSRI workshops are free of charge to attend, thanks to the generous support of funders including the National Science Foundation (NSF).

Image: When data automatically drop from the sky: intelligent approaches in data science change the way humans and computers interact. Illustration by Niklas Briner.Despite the remarkable success in extracting information from complex and (often) large-scale datasets over the last two decades, further progress is needed to making automated statistical and machine learning algorithms more reliable, robust, interpretable and trustworthy. This workshop has its focus on foundational aspects of this goal, linking areas at the interface between statistics, optimization, machine learning and computer science, such as distributional robustness and stability, adversarial and transfer learning, generalizability and meta analysis, and causality.

Workshop Organizers: Peter Bühlmann* (ETH Zürich), John Duchi (Stanford University), Elizabeth Tipton (Northwestern University), Bin Yu (University of California, Berkeley)

Featured Speakers: Mikhail Belkin (University of California, San Diego), Jose Blanchet (Stanford University), Tamara Broderick (Massachusetts Institute of Technology), Yuansi Chen (Duke University), Peng Ding (University of California, Berkeley), Raaz Dwivedi (Harvard University), Erin Hartman (University of California, Berkeley), Alex Madry (Massachusetts Institute of Technology), Blake McShane (Northwestern University), Jared Murray (University of Texas, Austin), Hongseok Namkoong (Columbia University), Betsy Ogburn (Johns Hopkins University), Jonas Peters (University of Copenhagen), Jacob Schauer (Northwestern University), Pragya Sur (Harvard University), Fanny Yang (ETH Zürich).

Contact: For more information, contact


Bin Yu

Professor and Second Chair, Departments of Statistics and EECS, UC Berkeley

Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the Departments of Statistics and of Electrical Engineering & Computer Science, and Center for Computational Biology at the University of California, Berkeley. She is an Investigator with the Weill Neurohub, a collaboration of the University of California, Berkeley (UC Berkeley), the University of California, San Francisco (UCSF), and the University of Washington (the UW). She leads the Yu Group at Berkeley, which is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group employs quantitative critical thinking and develops statistical and machine learning algorithms and theory. She has published more than 100 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, genomics, and networks. She was a Guggenheim Fellow and President of Institute of Mathematical Statistics (IMS), and is a member of the U.S. National Academy of Sciences and fellow of the American Academy of Arts and Sciences.