Basser Seminar Series

Highly scalable graphical modeling

Speaker: Professor Geoff Webb
Monash Centre for Data Science

When: Wednesday 30 August, 2017, 3-4pm please note different time to usual.

Where: The University of Sydney, School of IT Building, Boardroom (Room 124), Level 1

Abstract

Association discovery is a fundamental data mining task. The primary statistical approach to association discovery between variables is log-linear analysis. Classical approaches to log-linear analysis do not scale beyond about ten variables. By melding the state-of-the-art in statistics, graphical modeling, and data mining research, we have developed efficient and effective algorithms for log-linear analysis, performing in seconds log-linear analysis of datasets with thousands of variables and providing a powerful statistically-sound method for creating compact models of complex high-dimensional multivariate distributions. These techniques directly generalise to all main approaches to learning graphical models, providing exact methods with unparalleled scalability.

Speaker's biography

Geoff Webb is Director of the Monash Monash Centre for Data Science. He is a technical advisor to data science startup BigML. He has been Editor in Chief of the premier data mining journal, Data Mining and Knowledge Discovery (2005 to 2014) and Program Committee Chair of the two top data mining conferences, ACM SIGKDD (2015) and IEEE ICDM (2010), as well as General Chair of ICDM (2012). His primary research areas are machine learning, data mining, user modelling and computational structural biology. Many of his learning algorithms are included in the widely-used BigML, R and Weka machine learning workbenches. He is an IEEE Fellow and has received the 2013 IEEE ICDM Service Award, a 2014 Australian Research Council Discovery Outstanding Researcher Award, the 2016 Australian Computer Society ICT Researcher of the Year Award and the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award.