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Activity Number: 385 - Leo Breiman Award
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #300349 Presentation
Title: Integrating "two Cultures" in Data Science: Predictability, Computability, and Stability (PCS)
Author(s): Bin Yu*
Companies: UC Berkeley
Keywords: Predictability; computability; stability; machine learning; inference; data science life cycle

In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science: predictability, computability and stability (PCS). The related PCS workflow builds on machine learning, expands statistical inference, and covers the data science life cycle. It requires transparent documentation of narratives and codes. The three principles will be demonstrated in the context of two collaborative projects in neuroscience and genomics for interpretable data results and testable hypothesis generation.

Authors who are presenting talks have a * after their name.

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