CIS - Colloquium - Verdical data science for the practice of responsible data analysis and decision making by Prof. Bin Yu

Prof. Bin YU is currently Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California, Berkeley.

Title: Verdical data science for the practice of responsible data analysis and decision making

Abstract: Veridical data science aims at responsible, reliable, reproducible, and transparent data analysis and decision-making. Predictability, computability, and stability (PCS) are three core principles towards veridical data science. They embed the scientific principles of prediction and replication in data-driven decision making while recognizing the central role of computation. Based on these principles, the PCS framework consists of a workflow and documentation (in R Markdown or Jupyter Notebook) for the entire data science life cycle from problem formulation, data collection, data cleaning to modeling and data result interpretation and conclusions.
Employing the PCS framework in causal inference and analyzing data from clincial trial VIGOR, we developed staDISC for stable discovery of interpretable subgroups via calibration for precision medicine. The sugroups discovered by staDISC using the VIGOR data is validated to a good extent with the APPROVe study.


The Center for Intelligent Systems at EPFL (CIS) is a collaboration among IC, SB, and STI that brings together researchers working on different aspects of Intelligent Systems. In June 2020, CIS has launched its CIS Colloquia featuring invited notable speakers.
More info https://www.epfl.ch/research/domains/cis/prof-bin-yu/