University of California at Berkeley
Title: Three Principles of Data Science: Predictability, Stability, and Computability
In this talk, I’ll discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. The ultimate importance of prediction lies in the fact that future holds the unique and possibly the only purpose of all human activities, in business, education, research, and government alike. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results. It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. The three principles will be demonstrated through analytical connections, and in the context of two on-going projects, for which “data wisdom” is also indispensable. Specifically, the first project employs deep learning networks (CNNs) to understand pattern selectivities of neurons in the difficult visual cortex V4; and the second project predicts partisanship and tone of political TV ads by employing and comparing different latent variable models with a Lasso-based model.
Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley and a former Chair of Statistics at Berkeley. She is the founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. She is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, she develops statistics and machine learning methods/algorithms and theory and integrates with domain knowledge and quantitative critical thinking in the process. Bin Yu 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, an Invited Speaker at ICIAM in 2011, the Tukey Memorial Lecturer of the Bernoulli Society in 2012, and 2016 Rietz Lecturer of the Institute of Mathematical Statistics (IMS). She was President of IMS in 2013-2014, and is a Fellow of IMS, ASA, AAAS and IEEE.