BIDS Faculty Affiliate Bin Yu — Chancellor’s Professor of Statistics and of Electrical Engineering & Computer Sciences and a Principal Investigator of the Center for Computational Biology at UC Berkeley – has been selected to deliver the 2023 Wald Memorial Lectures at the Joint Statistical Meetings (JSM) in Toronto, Ontario, Canada, on August 5–10, 2023. This invitation is the highest honor bestowed by the Institute of Mathematical Statistics (IMS), and Dr. Yu joins a list of distinguished Berkeley colleagues who have received this honor, including her two PhD mentors, Lucien LeCam (1963) and Terry Speed (1991). The Wald Memorial Lecturer for the 2021 Joint Statistical Meetings was Jennifer Chayes, who is now the Associate Provost of the Division of Computing, Data Science, and Society, and Dean of the School of Information at UC Berkeley.
The Wald Memorial Lectures usually consists of two, three, or four individual lectures, each fifty to sixty minutes in length. Each lecture is given in a stand-alone session, which allows time for an introduction beforehand, and an extended question and answer session afterward. The first lecture is usually geared to a general audience, while subsequent lectures are often more specialized.
More information will be posted as this lecture series draws near. View more information about IMS lecture series, and videos of past IMS Wald Lectures on the IMS YouTube Channel.
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.