Speaker Biography
Mahesan Niranjan is Professor of Electronics and Computer Science at the University of Southampton. Prior to this appointment in 2008, he has held academic positions at the University of Cambridge as Lecturer in Information Engineering and at the University of Sheffield as Professor of Computer Science. At Sheffield, he also served as Head of Computer Science and Dean of Engineering. His research is in the area of Machine Learning, and he has worked on both the algorithmic and applied aspects of the subject. Some of his work has been fairly influential in the field – e.g. the SARSA algorithm widely used in the Reinforcement Learning literature. More recently, his focus of research is in data-driven inference problems in computational biology. More from: https://tinyurl.com/y5fnymel
Talk Abstract
While much of recent literature on machine learning address regression and classification problems, several problems of interest relate to detecting a relatively small number of outliers from large collections of data. Such problems have been addressed in the context of target tracking, condition monitoring of complex engines and patient health monitoring in an intensive care setting, for example. The popular approach, in these settings, of estimating a probability density over normal data and comparing the likelihood of a test observation against a threshold set from this suffers the well-known problem of the curse of dimensionality. Circumventing this involves modelling – data driven or otherwise – to capture known relationships in the data and looking for novelty in the residuals. This talk will describe several problems taken from the Computational Biology, Chemistry and Fraud Detection domains to illustrate this. We will discuss structured matrix approximation and tensor methods for multi-view data and suitable algorithms for their estimation.