Keynote Presentations

Prof Kate Smith-Miles, Monash University, Melbourne

What data mining can discover from your face ... no more lying about your age!

Speaker Biography
Kate Smith-Miles is a Professor and Head of the School of Mathematical Sciences at Monash University in Australia. Prior to commencing this role in January 2009, she held a Chair in Engineering at Deakin University (where she was Head of the School of Engineering and Information Technology from 2006-2008) and a Chair in Information Technology at Monash University, where she worked from 1996-2006. Her third Chair (in Mathematical Sciences), demonstrates her multi-disciplinary breadth, and she sees data mining as a truly multi-disciplinary endeavour. Kate obtained a B.Sc(Hons) in Mathematics and a Ph.D. in Electrical Engineering, both from the University of Melbourne, Australia.

She has published 2 books on neural networks and data mining applications, and over 175 refereed journal and international conference papers in the areas of neural networks, combinatorial optimization, intelligent systems and data mining. She has supervised to completion 16 PhD students, and has been awarded over AUD$1.75 million in competitive grants, including 8 Australian Research Council grants and industry awards. She is on the editorial board of several international journals including the prestigious IEEE Transactions on Neural Networks, and has been a member of the organizing committee for over 50 international data mining and neural network conferences, including several as chair.

She is a frequent reviewer of international research activities including grant applications in Canada, U.K., Finland, Hong Kong, Singapore and Australia, refereeing for international research journals, and PhD examinations. From 2007-2008 she was Chair of the IEEE Technical Committee on Data Mining (IEEE Computational Intelligence Society). She was elected Fellow of the Institute of Engineers Australia (FIEAust) in 2006, and Fellow of the Australian Mathematical Society (FAustMS) in 2008. She has been a Senior Member of the IEEE since 2001. In addition to her academic activities, she also regularly acts as a consultant to industry in the areas of optimisation, data mining, and intelligent systems.

Assoc Prof Jian Pei, Simon Fraser University, Canada

Towards Web Search Engine Scale Data Mining

Data mining is one of the most critical driving technologies behind Web search engines. Web search engine scale data mining posts many grand challenges, ranging from efficiency and scalability to diversity and adaptability. In this talk, I will review our recent effort on mining a very large amount of data accumulated in one of the major commercial search engines. Particularly, we tackle the problem of context-aware search and query suggestion by employing statistical models. Moreover, we construct a very large statistical model (millions of states) from a very large amount of data (billions of sessions) by distributed data mining. I will also introduce some of our recent initiatives in Web mining.

Speaker Biography
Jian Pei is currently an Associate Professor and the director of Collaborative Research and Industry Relations at the School of Computing Science, Simon Fraser University. His research interests include advanced techniques of data mining, data warehousing, online analytical processing, database systems, and information retrieval, as well as their applications in web search, sensor networks, health-informatics, bioinformatics, and business. His research has been supported extensively by government funding agencies and industry. He has published prolifically in refereed journals, conferences, and workshops and has served regularly in the organization committees and the program committees of many international conferences and workshops. He is a senior member of ACM and IEEE. He is the recipient of the British Columbia Innovation Council 2005 Young Innovator Award, an NSERC 2008 Discovery Accelerator Supplements Award, an IBM Faculty Award (2006), and the KDD'08 Best Application Paper Award.

Dr Ross Gayler, Veda Advantage, Melbourne

Credit scoring and data mining

Credit scoring is the use of predictive modelling techniques to support decision making in lending. It is a field of immense practical value that also supports a modest amount of academic research. Interestingly, the academic research tends not to be put into practice. This is not a result of insularity and arrogance on the part of the practitioners, but rather, of the practitioners having a better understanding of where they add value. This arises because credit scoring (and probably many other analytical applications) is dominated by shallow pragmatic issues rather than deep theoretical issues. In this talk I give examples of practical issues in credit scoring.

Speaker Biography
Ross Gayler is the Senior Research and Development Consultant for Veda Advantage, the largest and most sophisticated single source of data-based business intelligence in Australia and New Zealand. Among other things, Veda Advantage operates the main consumer credit bureaus in both countries and supplies predictive data for marketing and credit purposes. It also supplies analytical products and services in this area.

Ross was one of the principal developers of Veda's methodology and software for profitability prediction and optimisation. He has extensive experience in the development of predictive models for risk, marketing, fraud, and operational decision making in consumer finance. He entered the credit scoring area in 1989, building expert systems for lending. The most significant of these systems was in use for ten years with a major Australian institution. In 1992, Ross joined Experian (one of the major trans-national credit scoring and information companies) as a senior consultant and worked with major finance providers throughout Australia, New Zealand, and Asia. He also filled a research and development role to develop analytical methodologies for world-wide use by Experian. Subsequently, Ross has worked with the ANZ Bank and GE Money. In the former role he founded the bank's credit scoring team and in the latter role he established an advanced modelling function. His original qualifications are in psychology and computer science.

For further information contact Kok-Leong Ong (Deakin) or Paul Kennedy (UTS) through AusDM09@togaware.com

Last Modified Tuesday 2020-06-30 14:41:52 AEST Graham Williams