Dr Graham J Williams
Dr Graham Williams is Director of Data Science with Microsoft having responsibility for the Asian and Pacific region. His team of top data scientists with PhD’s in their respective fields of expertise, support some of the regions largest enterprises in developing their AI and Machine Learning capabilities on the Azure Cloud using Open Source and best of breed tooling.
Prior to joining Microsoft in 2016 he was Lead Data Scientist at the Australian Taxation Office and the Australian Government’s Data Analytics Centre of Excellence. As the technical leader of the Analytics capability in the Australian Taxation Office, he introduced and developed new technologies and processes for the analysis of big data in large organisations on a open source stack running over networks of Linux-based servers. His teams in the ATO developed and deployed many models to identify financial fraud and non-compliance, leading to significant financial returns for the Australian Government and increase compliance. Through the Data Analytics Centre of Excellence he advised other government departments in setting up their Data Analytics capabilities.
Prior to join the Australian Government Graham was Principal Computer Scientist for Data Mining with CSIRO Australia (1991-2004) and a Senior International Expert and Visiting Professor of the Chinese Academy of Sciences at the Shenzhen Institutes of Advanced Technologies. He is also Adjunct Professor, Data Mining, Fraud Prevention, Security, University of Canberra, and Australian National University.
Graham has been involved in data mining since the 1980s as a researcher and practitioner. He has lead projects with clients including the Health Insurance Commission, the Australian Taxation Office, the Commonwealth Bank, NRMA Insurance Limited, the Commonwealth Department of Health and Ageing, Queensland Health, and the Australian Customs Service. He has developed open source software and hardware environments for data mining, and pioneered the implementation of web services for the delivery of data mining in the 1990’s. His research developments include Multiple (i.e., Ensemble) Decision Tree Induction (1989), HotSpots for identifying target areas in very large data collections (1992), WebDM for the delivery of data mining services over the web using XML (1995), and Rattle (2005), a simple to use Graphical User Interface designed to make data mining accessible for data analysts.
Graham’s popular text book on Data Mining with Rattle and R was published by Springer in 2011 with a followup text book, The Essentials of Data Science: Knowledge Discovery Using R, published in 2017. His OnePageR website is an increasingly popular resource for data miners using R and together with his MLHub project, and his Data Science textbooks, are widely used for teaching Data Science at universities world wide.
Graham is involved in numerous international artificial intelligence and data mining research activities and conferences, as founder and chair of the Australasian Data Mining Conference and Chair of the steering committees of the Pacific Asia Knowledge Discovery and Data Mining conference. He is also a member of the steering committee of the Australian Artificial Intelligence conference. His research interests since the 1980’s has covered many aspects of artificial intelligence, machine learning, data mining and very large databases. He has edited a number of books and has authored many academic and industry papers.
Graham’s PhD (Australian National University, 1991) introduced the then novel concept of building multiple predictive models (decision trees) and then combining them into a single model to achieve better predictive capabilities. Such ensemble approaches are now widely used and recognised as providing significant gains for modelling.
Graham has worked for a number of research and industrial organisations including: CSIRO Land and Water in Canberra, Australia, developing award wining spatial expert systems in the 1980s; BBJ Computers, Melbourne, Australia, as Research and Development and then Marketing Manager, overseeing the pioneering implementation a data mining tool (decision tree induction) integrated within a 4GL database environment (1987); Vish Corporation, involved in developing one of the first commercial and longest running Expert Systems in Australia (1989) for credit prediction (using decision trees) for Esanda Finance (ANZ Bank), Melbourne, Australia; and the Australian National University, Canberra, lecturing in Database Systems, Machine Learning, Data Mining, and Software Engineering.
Today, Graham regularly teaches courses in data mining, internationally, and actively participates in research, particularly through his role with the Chinese Academy of Sciences and Australian National University.