CCAIM Seminar Series – Prof Bin Yu - UC Berkeley

Topic: Predictability, stability, and causality with a case study to seek genetic drivers of a heart disease
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For this event, Prof Yu is hosted by Prof Mihaela van der Schaar of the University of Cambridge, Director of the Cambridge Centre for AI in Medicine.
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About the speaker

Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS, and Center for Computational Biology, all 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 precision 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 holds a Honorary Doctorate from the University of Lausanne, and served on the inaugural scientific advisory committee of the UK Turing Institute for Data Science and AI. She has been serving on the editorial board of Proceedings of National Academy of Sciences (PNAS).
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Details of presentation

"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 and beyond. Human judgement calls are ubiquitous at every step of a data science life cycle, 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 introduce in this talk a framework based on three core principles: Predictability, Computability and Stability (PCS). The PCS framework unifies and expands on the best practices of machine learning and statistics. PCS emphasizes reality check through predictability and takes a full account of uncertainty sources in the whole data science life cycle including those from human judgment calls such as those in data curation/cleaning. PCS consists of
a workflow and documentation and is supported by our software package v-flow.

Next we illustrate the usefulness of PCS in development of of iterative random forests (iRF) for predictable and stable non-linear interaction
discovery (in collaboration with the Brown Lab at LBNL and Berkeley Statistics).

Finally, in the pursuit of genetic drivers of a heart disease called hypertrophic cardiomyopathy (HCM) as a CZ Biohub project in collaboration with the Ashley Lab at Stanford Medical School and others, we use iRF and UK Biobank data to recommend gene-gene interaction targets for knock-down experiments. We then analyze the experimental data to show promising findings about genetic drivers of HCM.