In this talk, we learned from BIDS Faculty Affiliate Bin Yu about her foundational work on a new data science framework. This was part of IEOR's Foundations of Data Science – Virtual Talk Series.
Veridical Data Science
Date: Friday Sept 11, 2020
Time: 10:00 AM Pacific / 1:00 PM Eastern
Location: Register to attend this talk
Speaker: Bin Yu, Chancellor’s Professor in the departments of Statistics and of Electrical Engineering & Computer Science, UC Berkeley
Abstract: Building and expanding on principles of statistics, machine learning, and the sciences, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework is comprised of both a workflow and documentation and aims to provide responsible, reliable, reproducible, and transparent results across the entire data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle for the data science life cycle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. We develop inference procedures that build on PCS, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations.
Moreover, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis.
The PCS framework will be illustrated through our DeepTune approach to model and characterize neurons in the difficult visual cortex area V4.
Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley. She is founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. Her group at Berkeley is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group employs quantitative critical thinking and develops statistical and machine learning algorithms and theory. She has published more than 100 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, genomics, and networks. She is a member of the U.S. National Academy of Sciences and fellow of the American Academy of Arts and Sciences.