Speaker: Professor Bin Yu, Chancellor's Professor, Departments of Statistics and Electrical Engineering & Computer Science, UC Berkeley    

Title: Veridical Data Science for Biomedical Research: detecting epistatic interactions via epiTree

Recording of the talk.

Abstract: "A.I. is like nuclear energy -- both promising and dangerous" -- Bill Gates, 2019.

Data Science is a pillar of A.I. and has driven most of recent cutting-edge discoveries in biomedical research. In practice, Data Science has a life cycle (DSLC) that includes problem formulation, data collection, data cleaning, modeling, result interpretation and the drawing of conclusions. Human judgement calls are ubiquitous at every step of this process, e.g., in choosing data cleaning methods, predictive algorithms and data perturbations. Such judgment calls are often responsible for the "dangers" of A.I.

To maximally mitigate these dangers, we developed a framework based on three core principles: Predictability, Computability and Stability (PCS). Through a workflow and documentation (in R Markdown or Jupyter Notebook) that allows one to manage the whole DSLC, the PCS framework unifies, streamlines and expands on the best practices of machine learning and statistics – bringing us a step forward towards veridical Data Science.

In this lecture, we will illustrate the PCS framework through the epiTree; a pipeline to discover epistasis interactions from genomics data. epiTree addresses issues of scaling of penetrance through decision trees, significance calling through PCS p-values, and combinatorial search over interactions through iterative random forests (which is a special case of PCS). Using UK  Biobank data, we validate the epiTree pipeline through an application to the red-hair phenotype, where several genes are known to display epistatic interactions.

Biography: Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the Department of Statistics and EECS at UC Berkeley. She leads the Yu Group which consists of 15-20 students and postdocs from Statistics and EECS. She was formally trained as a statistician, but her research extends beyond the realm of statistics. Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics and precisio medicine. She and her team develop relevant theory to understand random forests and deep learning for insight into and guidance for practice.

She is a member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott prize winner. She is serving on the editorial board of Proceedings of National Academy of Sciences (PNAS) and the scientific advisory committee of the UK Turing Institute for Data Science and AI.

List of Distinguished Speaker Seminars.