Oppenheim Lecture by Bin Yu – Institute for Mathematical Science

(29 Nov 2024–02 Dec 2024)

Description

Oppenheim Lecture (Jointly organized with Department of Mathematics, NUS)

The Oppenheim Lectures is a distinguished lecture series jointly organized by the Department of Mathematics, and the Institute for Mathematical Sciences at the National University of Singapore (NUS). It is held annually beginning from the Academic Year 2014/2015, and is in honour of Sir Alexander Oppenheim, who held the position of Professor and first Head of the Department from 1931 until 1959. Professor Oppenheim was also Vice Chancellor of the University of Malaya (the predecessor of NUS) from 1957 to 1963. He was a well-known number theorist, notably for the Oppenheim Conjecture, which was settled by Gregori Margulis in the affirmative in 1986.

Overview

About the speaker

Professor Bin Yu is CDSS Chancellor’s Distinguished Professor in Statistics, EECS, and Computational Biology, and Scientific 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, 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, and adaptive wavelet distillation (AWD) from deep learning models. She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow, IMS President, and delivered the IMS Rietz and Wald Lectures and Distinguished Achievement Award and Lecture (formerly Fisher Lecture) of COPSS. She holds an Honorary Doctorate from The University of Lausanne.

About the Talk

The rapid advancement of AI relies heavily on the foundation of data science, yet its education significantly lags its demand in practice. A new book ‘Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making’ by Yu and Barter was published in Oct, 2024 by the MIT Press (free online at www.vdsbook.com). It tackles this gap by promoting Predictability, Computability, and Stability (PCS) as core principles for trustworthy data insights. PCS for veridical data science (VDS) has been developed in the process of solving scientific data science problems. It thoroughly integrates these principles into the Data Science Life Cycle (DSLC), from problem formulation to data cleansing and to result communication, fostering a new standard for responsible data analysis. This talk explores PCS’ motivations and compares the VDS book approaches with traditional ones. I will end the talk with a PCS-guided project on prostate cancer detection.