Speaker: Bin Yu (University of California, Berkeley)

Title: Veridical Data Science and PCS Uncertainty Quantification

Abstract: Data Science is central to AI and has driven most of the recent advances in biomedicine and beyond. Human judgment calls are ubiquitous at every step of the data science life cycle (DSLC). We will introduce Veridical (truthful) Data Science (VDS) based on three core principles of data science: Predictability, Computability and Stability (PCS) to formally take into account the human judgment calls as sources of uncertainty. PCS will be showcased through collaborative research in prostate cancer detection and in seeking genetic drivers of a heart disease. We will end with on-going research on PCS uncertainty quantification (UQ) that addresses two unconventional prominent sources of uncertainty in the DSLC from data cleaning and algorithm choices.

In person or on Zoom at https://mit.zoom.us/j/93513735220