Distinguished Lecture Series

David Sprott Distinguished Lecture by Professor Peter Diggle, Lancaster University

Greg Preston — Tue, 14 Mar 2017 13:43:55 +0000

David Sprott Distinguished Lecture by Professor Peter Diggle, Lancaster University Greg Preston Tue, 03/14/2017 - 09:43

A Tale of Two Parasites: how can Gaussian processes contribute to improved public health in Africa?

In this talk, I will rst make some general comments about the role of statistical modelling in scientic research, illustrated by two examples from infectious disease epidemiology. I will then describe in detail how statistical modelling based on Gaussian spatial stochastic processes has been used to construct region-wide risk maps to inform the operation of a multi-national control programme for onchocerciasis (river blindness) in equatorial Africa. Finally, I will describe work-in progress aimed at exploiting recent developments in mobile microscopy to enable more precise local predictions of community-level risk.

About Peter Diggle:

Peter Diggle is a Distinguished University Professor of Statistics in the Faculty of Health and Medicine, Lancaster University. He also holds Adjunct positions at Johns Hopkins, Yale and Columbia Universities, and was president of the Royal Statistical Society between July 2014 and December 2016. Peter began his academic career at the University of Newcastle upon Tyne in 1974, moved to Australia in 1984 as a research scientist with the Commonwealth Scientific and Industrial Research Organisation and returned to the UK in 1988 to take up his current post in Lancaster. His research involves the development of statistical methods for spatial and longitudinal data analysis, and their application to substantive research in the biomedical and health sciences.

David Sprott Distinguished Lecture by Professor David Donoho, Stanford University

Ryan Ball — Tue, 04 Oct 2016 19:13:19 +0000

David Sprott Distinguished Lecture by Professor David Donoho, Stanford University Ryan Ball Tue, 10/04/2016 - 15:13

Factor Models and PCA in light of the spiked covariance model

Principal components analysis and Factor models are two of the classical workhorses of high-dimensional data analysis, used literally thousands of times a day by data analysts the world over. But now that we have entered the big data era, where there are vastly larger numbers of variables/attributes being measured that ever before, the way these workhorses are deployed needs to change.

In the last 15 years there has been tremendous progress in understanding the eigenanalysis of random matrices in the setting of high-dimensional data in particular progress in understanding the so-called spiked covariance model. This progress has many implications for changing how we should use standard `workhorse' methods in high-dimensional settings. In particular it vindicates Charles Stein's seminal insights from the mid 1950's that shrinkage of eigenvalues of covariance matrices is essentially mandatory, even though today such advice is still frequently ignored. We detail new shrinkage methods that flow from random matrix theory and survey the work of several groups of authors.

About Dr. Donoho:

vid L. Donoho is a Professor of Statistics and the Anne T and Robert M Bass Professor of the Humanities and Sciences at Stanford University. He earned his AB in Statistics from Princeton and his PhD in Statistics from Harvard. He began his career in the Department of Statistics at the University of California Berkeley and later moved to the Department of Statistics at Stanford University. He has also worked for Western Geophysical Company and Renaissance Technologies. He was co-founder of network management software company BigFix. His publication list covers Robust Statistics, Signal and Image Processing, Mathematical Statistics, Harmonic Analysis, Scientific Computing, and High Dimensional Geometry. He has made ground-breaking contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms contributed profoundly to the understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.

He is a member of the United States National Academy of Sciences as well as a foreign associate of the Academie des Sciences of France and has been named a MacArthur Fellow, a Fellow of the American Academy of Art and Sciences, a Fellow of the Society for Industrial and Applied Mathematics, and a Fellow of the American Mathematical Society. He has received the COPSS Presidents’ Award, the John von Neumann Prize, and the Norbert Wiener Prize. He holds an honorary doctorate from the University of Chicago and in 2013 became a Shaw Prize Laureate in the Mathematical Sciences.

David Sprott Distinguished Lecture by Professor David Donoho Poster (PDF)

David Sprott Distinguished Lecture by Jack Kalbfleisch, University of Michigan, Ann Arbor

Ryan Ball — Thu, 08 Sep 2016 17:01:53 +0000

David Sprott Distinguished Lecture by Jack Kalbfleisch, University of Michigan, Ann Arbor Ryan Ball Thu, 09/08/2016 - 13:01

Match making in a Kidney Paired Donation Program

A kidney-paired donation program (KPDP) consists of transplant candidates and their incompatible donors, along with non-directed donors (NDDs), who are willing to donate a kidney to the program. The aim of the KPDP is to arrange matches of donors and candidates in order to overcome incompatibilities. A virtual crossmatch based on blood types of a candidate and donor as well as donor HLA antigens and candidate sensitivities can be used to identify potential transplants. Unfortunately, however, an identified potential transplant often cannot proceed (is not viable) because of illness or schedule conflicts or because an incompatibility is identified on a definitive laboratory crossmatch. A given KPDP can be represented as a directed graph with edges indicating a potential transplant, and transplants can be carried out based on disjoint cycles of pairs and chains created from NDDs. A problem of substantial importance is how to select potential transplants for consideration in order to optimize the number of transplants achieved and I will discuss and compare various approaches to this. Our approach takes account of probabilities that potential transplants are viable and seeks selections that keep many options that can be implemented depending on viability.

Event Poster for David Sprott Distinguished Lecture.pdf

About Dr. Kalbfleisch:

Dr. Kalbfleisch is Emeritus Professor of Biostatistics and Statistics at the University of Michigan and Distinguished Emeritus Professor at the University of ��ݮ��Ƶ. He received his Ph.D. in 1969 from ��ݮ��Ƶ and was an assistant professor of statistics at the State University of New York at Buffalo (19-73). He was on faculty at ��ݮ��Ƶ (1973-2002) and served as chair of Statistics and Actuarial Science (1984-90) and dean of Mathematics (1990-98). He also served as chair of the Department of Biostatistics at Michigan (2002-07) and as Director of the Kidney Epidemiology and Cost Center (2008-11). He has published in various areas of statistics and biostatistics including life history and survival analysis, likelihood methods of inference, mixture and mixed effects models and medical applications, particularly in the area of renal disease and organ transplantation. He is a Fellow of the Royal Society of Canada and a Gold Medalist of the Statistical Society of Canada.