Public Lecture by Trevor Hastie

Date: 

Thursday, 8 March 2018 - 6:00pm

Venue: 

Science Theatre

Public lecture by Trevor Hastie: Statistical Learning with Big Data

 
The School of Mathematics and Statistics is proud to present a free public lecture by Trevor Hastie, Stanford Trevor HastieUniversity, one of the most renowned names in the mathematical sciences.

Abstract:

As our ability to gather and store data improves, we are faced with the task of analysing these ever-growing mounds of information.  This required Statisticians to gain computing and database skills, and Engineers and Computer Scientists to learn statistical modeling and data analysis.  The result is a data scientist, one of the hottest job-descriptions in the tech world.

In this talk I will give some examples of big data and data-science challenges, and explore some approaches in detail.


Biography: 

Trevor Hastie has one of the most recognised names in the mathematical sciences. He has made significant contributions to applied regression, being instrumental in the development of a number of widely used analysis tools including generalised additive models, the LASSO and its extensions, and the “gap” statistic (with R. Tibshirani, J. Friedman and collaborators). 

He is well known for his texts that make advanced statistical tools accessible to researchers across disciplines, in particular "Generalized Additive Models" (with R. Tibshirani, Chapman and Hall, 1991), "Elements of Statistical Learning" (with R. Tibshirani and J. Friedman, Springer 2001; second edition 2009), and more recently "Statistical Learning with Sparsity" (with R. Tibshirani and M. Wainwright, Chapman and Hall, 2015) and "Computer Age Statistical Inference" (with Bradley Efron, Cambridge 2016). 

He has also made significant contributions in statistical computing, co-editing (with J. Chambers) a large software library on modelling tools in the S language ("Statistical Models in S", Wadsworth, 1992), which form the foundation for many statistical modelling tools in R that are widely used today. His current research focuses on applied statistical modelling and prediction problems in biology and genomics, medicine and industry.

Trevor is the John A. Overdeck Professor of Mathematical Sciences at Stanford University. He was born in South Africa and started his studies there (at Rhodes University and University of Cape Town), then completed a PhD at Stanford University in 1984.  He spent eight years at AT&T Bell Laboratories before returning to Stanford as a Professor in Statistics and Biostatistics. 

He currently holds a part-time SHARP Professorial appointment at UNSW Sydney in the School of Mathematics and Statistics.