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Bin Yu – Veridical Data Science (Foundations of Data Science – Virtual Talk Series)
September 11, 2020 @ 10:00 am - 11:00 pm
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 The Class of 1936 Second Chair in the College of Letters and Science, and Chancellor’s Distinguished Professor, Departments of Statistics and of Electrical Engineering & Computer Sciences, University of California at Berkeley and a former chair of Statistics at UC Berkeley.
She heads 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 interests and achievements extend 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, remote sensing, and precision medicine. She and her group also develop relevant theory to provide insight and guide practice.
She is a member of the U.S. National Academy of Sciences and a fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She received the E. L. Scott Award from COPSS (Committee of Presidents of Statistical Societies) in 2018.