Dear Colleagues,
This is a reminder that the next SLDS webinar is on this Thursday. Prof. Bin Yu from UC Berkeley, member of National Academy of Science, will discuss about veridical data science and the PCS (Predictability, Computability and Stability) framework in data science practice. There is still time to register. Hope to see you there!
Title: Why Veridical Data Science? And How?
Speakers: Dr. Bin Yu, Departments of Statistics and Electrical Engineering and Computer Sciences, UC Berkeley
Date and Time: April 4, 2024, 2:00 to 3:30 pm Eastern Time
Registration Link: ASA SLDS Webinar Registration Link [eventbrite.com]
Abstract:
"AI is like nuclear energy–both promising and dangerous." -- Bill Gates, 2019
Data Science is central to AI and has driven most of recent advances in biomedicine and beyond. Human judgment calls are ubiquitous at every step of a data science life cycle (DSLC): problem formulation, data cleaning, EDA, modeling, and reporting. Such judgment calls are often responsible for the "dangers" of AI by creating a universe of hidden uncertainties well beyond sample-to-sample uncertainty.
To mitigate these dangers, veridical (truthful) data science is introduced based on three principles: Predictability, Computability and Stability (PCS). The PCS framework and documentation unify, streamline, and expand on the ideas and best practices of statistics and machine learning. In every step of a DSLC, PCS emphasizes reality check through predictability, considers computability up front, and takes into account expanded uncertainty sources including those from data curation/cleaning and algorithm choice to build more trust in data results. PCS will be showcased through collaborative research in identifying microbiome-related metabolite signatures and reducing number of gene expressions for possible early detection of pancreatic and prostate cancers, respectively. Last but not least, we will discuss the differences between a traditional book and the new MIT Press book by Yu and Barter "Veridical data science: the practice of responsible data analysis and decision making" (vdsbook.com) and how to use the VDS book alone or together with a traditional book in a stats/DS/ML class.
If you want to learn more about Veridical Data Science (VDS), consider registering at the following conference:
Presenter: Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in Statistics, EECS, and Computational Biology at UC Berkeley. Her research focuses on the practice and theory of statistical machine learning, veridical data science, and solving interdisciplinary data problems in neuroscience, genomics, and precision medicine. She and her team have developed algorithms such as iterative random forests (iRF), stability-driven NMF, and adaptive wavelet distillation (AWD) from deep learning models. She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow, and holds an Honorary Doctorate from The University of Lausanne.
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Zhihua Su, PhD
Associate Professor
Department of Statistics
University of Florida
zhihuasu@stat.ufl.edu------------------------------