2019 Program

Thursday, February 7, 2019, 4:30pm - 5:45pm

Jen-Hsun Huang Engineering Center - Mackenzie Room (Huang 300)
Stanford University
475 Via Ortega, Stanford, CA 94305

Directions & Parking
A map showing the location of the Jen-Hsun Huang Engineering Center (Huang) can be found here.

The nearest parking is Via Ortega Garage/Parking Structure 2, located on the corner of Panama Street and Via Ortega Avenue. Pay parking is available on the ground and lower levels.

For a searchable campus map, please visit Stanford Visitor Maps.

Flyer (PDF)

The 2019 Kailath Lecture
Featured Speaker: Emmanuel Candès
Barnum-Simons Chair in Math and Statistics
Professor of Electrical Engineering by Courtesy
Stanford University
Talk Sailing Through Data: Discoveries and Mirages
Abstract For a long time, science has operated as follows: a scientific theory can only be empirically tested, and only after it has been advanced. Predictions are deduced from the theory and compared with the results of decisive experiments so that they can be falsified or corroborated. This principle formulated by Karl Popper and operationalized by Ronald Fisher has guided the development of scientific research and statistics for nearly a century. We have, however, entered a new world where large data sets are available prior to the formulation of scientific theories. Researchers mine these data relentlessly in search of new discoveries and it has been observed that we have run into the problem of irreproducibility. Consider the April 23, 2013 Nature editorial: “Over the past year, Nature has published a string of articles that highlight failures in the reliability and reproducibility of published research.” The field of Statistics needs to re-invent itself to adapt to the new reality where scientific hypotheses/theories are generated by data snooping. We will make the case that statistical science is taking on this great challenge and discuss exciting achievements. In particular, we will introduce the method of knockoffs, which reliably selects which of the many potentially explanatory variables of interest (e.g. the absence or not of a mutation) are indeed truly associated with the response under study (e.g. the risk of getting a specific form of cancer). We will see that this line of work opens up new perspectives in applied probability, in scientific computing, and even in deep learning.
Bio Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of Electrical Engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Prof. Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.

Prof. Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014.