Yiying Zhang
Southern University of Science and Technology
Room: M3 3127
Dennis Lin
Purdue University
Room: M3 3127
Stanislav Uryasev
Stony Brook University
Room: M3 3127
Dean's Distinguished Women in Mathematics, Statistics and Computer Science Lecture Series & David Sprott Distinguished Lecture Series
Bin Yu
CDSS Chancellor's Distinguished Professor, Statistics, EECS, Center for Computational Biology
Senior Advisor, Simons Inst for the Theory of Computing
Member, U.S. National Academy of Sciences, 2014
Member, American Academy of Arts and Sciences, 2013
Guggenheim Fellow, 2006
Room: DC 1302
In this talk, I will introduce the Predictability-Computability-Stability (PCS) framework for veridical (truthful) data science, highlighting its critical role in producing reliable and actionable insights. I will share success stories from cancer detection and cardiology, showcasing how PCS principles have guided cost effective designs and improved outcomes in these projects. Since trustworthy uncertainty quantification is indispensable for trustworthy AI, I will discuss PCS uncertainty quantification for prediction in regression and multi-class classification. PCS-UQ consists of three steps: pred-check, bootstrap, and multiplicative calibration. Through test results over 26 benchmark datasets, PCS-UQ will be shown to outperform common forms of conformal prediction in terms of width, subgroup coverage, and subgroup interval width. Finally, the multiplicative step in PCS-UQ will be shown to be a new form of conformal prediction.
Tim Swartz
Simon Fraser University
Room: M3 3127
Peng Shi
University of Wisconsin-Madison
Room: M3 3127
Bin Zou
University of Connecticut
Room: M3 3127
Michael Kupper
University of Konstanz
Room: M3 3127
Marcel Nutz
Columbia University
Room: M3 3127
This is a free event, but please register for this talk on CANSSI's website.
Saeed Ghadimi
University of Waterloo
Room: M3 3127