Challis Lecture: Fruit Flies in Statistics and Machine Learning, Dr. John Lafferty, Yale University


4:00 pm-5:00 pm
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Reitz Union Chamber
J. Wayne Reitz Union
Gainesville, FL 32611


Fruit Flies in Statistics and Machine Learning

John D. Lafferty, Professor of Statistics and Data Science, Yale University

Abstract: The fruit fly is the model scientific organism, employed as a laboratory animal to study the principles of life. Important advances in statistical methodology and theory have come from probing various statistical “fruit flies,” as we illustrate with some examples. However, the use of fruit flies is less prevalent in recent machine learning research. With the accelerating use of machine learning to engineer complex systems, it is increasingly difficult to understand the statistical principles at play. We argue that there is a need for more methodical inquiry through the study of simpler “organisms” in machine learning research.Traditional statistical perspectives can be of service in this effort, but there is also room for
more empirical approaches.

Professor John D. Lafferty is the John C. Malone Professor of Statistics and Data Science. He is a world-renowned expert on statistical machine learning, with a focus on computational and statistical aspects of nonparametric methods, high-dimensional data, graphical models, and statistical language modeling. Lafferty earned his doctoral degree in mathematics from Princeton University, where he was a member of the Program in Applied and Computational Mathematics. He worked as a research staff member at the IBM Thomas J. Watson Research Center, before joining the faculty at Carnegie Mellon University. He was previously the Louis Block Professor of Statistics and Computer Science at
the University of Chicago before joining Yale in July 2017 as professor of statistics and data science, with a secondary appointment in computer science. He has won four “Test of Time” awards from the International Conference on Machine Learning and in 2015, he delivered an Institute of Mathematical Statistics Medallion Lecture.


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