School of Informatics

David Dunson - Machine learning for scientific inferences: Debunking the hype

Professor David Dunson

Speaker

Professor David Dunson, Arts & Sciences Distinguished Professor, Duke University, USA and Carnegie Centenary Professor 2018, University of Edinburgh.

Title

Machine learning for scientific inferences: Debunking the hype

Date

Monday 28th May 2018 (16:00 - 17:00) followed by a Drinks Reception

Recording of the event

Abstract

There is understandably huge excitement about the remarkable success of machine learning (ML) algorithms, such as “deep learning”, in a variety of domains ranging from pattern recognition in imaging, to self-driving cars. This excitement has generated increasing hype and an expectation that we can solve challenging problems in scientific inferences from modern complex data sources using recent ML/AI tools.  However, I would argue that the success stories are very special in involving highly structured data (in space/time) and tasks that humans are very good at, with the possibility of leveraging on abundant training data.  In sharp contrast, most scientific data are very complex and high-dimensional without a known structure to exploit, with very limited training data, and with most of the focus being on understanding relationships and latent structure and not on prediction.  Using applications in neuroscience, ecology and genomics as motivation, I debunk the hype that current ML/AI tools are at all good at solving key problems of interest, and suggest some promising strategies and areas of new research. 

Biography

David Dunson is Arts and Sciences Distinguished Professor of Statistical Science, Mathematics, and Electrical & Computer Engineering at Duke University.  His research focuses on Bayesian statistical theory and methods motivated by high-dimensional and complex applications.  A particular emphasis is on dimensionality reduction, scalable inference algorithms, latent factor models, and nonparametric approaches, particularly for high-dimensional, dynamic and multimodal data, including images, functions, shapes and other complex objects.  His work involves inter-disciplinary thinking at the intersection of statistics, mathematics and computer science.  Motivation comes from applications in epidemiology, environmental health, neurosciences, genetics, fertility and other settings (music, fine arts, humanities).  Professor Dunson is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.  He is winner of the 2007 Mortimer Spiegelman Award for the top public health statistician under 41, the 2010 Myrto Lefkopoulou Distinguished Lectureship at Harvard University, the 2010 COPSS Presidents' Award for the top statistician under 41, and the 2012 Youden Award for inter-laboratory testing methods.

 

Carnegie Trust and the University of Edinburgh logos
May 28 2018 -

David Dunson - Machine learning for scientific inferences: Debunking the hype

Professor David Dunson will be giving a talk about machine learning for scientific inferences.

Lecture: G.07/G.07a, Informatics Forum, University of Edinburgh | (Drinks Reception: Informatics Forum)