Three Principles for Data Science: Predictability, Stability and Computability

P_02_Bin Yu(Photo)
Bin Yu
UC Berkeley

Abstract :

In this talk, I’d like to 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 founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. Her group at Berkeley is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group develops statistics and machine learning algorithms and theory while integrating with quantitative critical thinking.


Professional Activities

  • Member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences.
  • Fellow of IMS, ASA, AAAS and IEEE.
  • 2013-2014 President of IMS.
  • 2012 the Tukey Memorial Lecturer of the Bernoulli Society.
  • 2011 an Invited Speaker at ICIAM.
  • 2006 a Guggenheim Fellow.