Science at the Shine Dome 2010
Symposium: Genomics and mathematics
Friday, 7 May 2009
Dr Iain Johnstone
Iain Johnstone is Marjorie Mhoon Fair Professor in Quantitative Science in the Department of Statistics at Stanford University. He holds a joint appointment in biostatistics in Stanford’s School of Medicine. He received his PhD in statistics from Cornell in 1981.
Iain’s work in theoretical statistics aims to provide insight into methods of data analysis in diverse areas of science and medicine. He has used ideas from harmonic analysis, such as wavelets, to understand noise-reduction methods in signal and image processing. More recently, he has applied random matrix theory to the study of high-dimensional multivariate statistical methods, such as principal components and canonical correlation analysis. In biostatistics, he has collaborated extensively with investigators in cardiology and prostate cancer.
A native of Australia, Iain is a member of the US National Academy of Sciences and the American Academy of Arts and Sciences and a former president of the Institute of Mathematical Statistics.
Extremes of variation in high-dimensional data
When data are collected on many, possibly related, variables it is natural to seek informative summaries using a small number of derived variables. Traditional methods such as principal components and canonical correlation analysis seek to do this, but the large number of variables poses a challenge to assessments of statistical significance. In recent years, progress in random matrix theory has used the high dimensionality as an opportunity rather than a problem. In my talk I will give a non-technical overview and try to make connections with the use of principal components analysis in genome-wide association studies.
