NISS AI, Statistics & Data Science in Practice: Bin Yu - Veridical Data Science (10/15/2024)

Date: Tuesday, October 15, 2024 - 11:30am to 1:00pm ET

Full details available on the event page here: https://www.niss.org/events/niss-ai-s...

Speaker: Bin Yu, Class of 1936 Second Chair in the College of Letters and Science, and Chancellor's Distinguished Professor, Departments of Statistics and of Electrical Engineering & Computer Sciences, University of California at Berkeley; and Simons Institute

Moderator: Nancy McMillan, Data Science Research Leader within Battelle’s Health Research & Analytics Business Line

Abstract: The rapid advancement of AI relies heavily on the foundation of data science, yet its education significantly lags its demand in practice. The upcoming book 'Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making' (Yu and Barter, MIT Press, 2024; free online at www.vdsbook.com) 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 compare the VDS book appraoch with traditional ones. Then I will describe two PCS projects on prostate cancer detection and discovery of epistastic genetic drivers for a heart disease. I will end with on-going work for PCS uncertainty quantificaiton in regression and its comparison with conformal prediction, PCS software pacakages (v-flow, simChe), and MEIRTS guidelines for data-inspired simulations.

View the full playlist on our YouTube Channel: AI, Statistics and Data Science in Practice webinar series:    • NISS AI, Statistics and Data Science in Pr...