2018 Hogg and Craig Lecturer is David Donoho

Anne T. and Robert M. Bass Professor of Humanities and Sciences and Professor of Statistics, Stanford University
Thursday, April 26, 2018 to Friday, April 27, 2018

Dr. David Donoho from Stanford University will be our 46th Hogg and Craig Lecturer.  

Early in the 1969-70 academic year, Professor Allen T. Craig announced his retirement. He gave a retirement talk in January 1970. Under the leadership of Craig’s student and co-author, Professor Robert V. Hogg, the department decided to establish a lecture series to honor Professor Craig. His January 1970 talk was the first in this series. When Professor Hogg passed away at the age of 90 in 2014, the department decided to incorporate his name into the lecture series.



Thursday, April 26

11:00-11:45 a.m. Refreshments and Awards in 302 Schaeffer Hall

3:30 p.m. Lecture #1 in W151 Pappajohn Business Building (PBB)

50 Years of Data Science

Dr. Donoho will review the article by the same title appearing in the December 2017 issue of “Journal of Computational and Graphical Statistics.” Some passages from the abstract of that paper:

More than 50 years ago, John Tukey called for a reformation of academic statistics. In “The Future of Data Analysis,” he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or “data analysis.” 


This article reviews some ingredients of the current “data science moment,” including recent commentary about data science in the popular media, and about how/whether data science is really different from statistics.


Drawing on work by Tukey, Cleveland, Chambers, and Breiman, Dr. Donoho will present a vision of data science based on the activities of people who are “learning from data,” and will describe an academic field dedicated to improving that activity in an evidence-based manner.


Friday, April 27

3:00 p.m. Reception in 241 Schaeffer Hall

3:30 p.m. Lecture #2 in Shambaugh Auditorium, Main Library

Covariance Estimation in Light of the Spiked Covariance Model

Since Charles Stein's pioneering work in 1956, we know that high-dimensional covariance estimation requires shrinkage. Owing to recent progress in the so-called spiked covariance model, we can now precisely determine the structure of optimal orthogonally-equivariant estimates of covariance matrices, under each of many different metrics. Dr. Donoho will describe the optimal procedures and some of their interesting properties.

The talk will review the article Optimal Shrinkage of Eigenvalues in the Spiked Covariance Model currently available on-line at the 'papers to appear in future issues' website of the Annals of Statistics. It will also review subsequent work.

This is joint work with Iain Johnstone, Matan Gavish, Behrooz Ghorbani.