Dr Graham J Williams
(A Long Bio is seperately available.)
Dr Graham Williams is Director of Data Science, Microsoft Asia. He is an international data science advocate, practitioner, researcher, and educator. He has authored many books and papers as well as several popular R packages including Rattle and pmml. As Lead Data Scientist at the Australian Taxation Office from 2004 until 2016 he was involved in setting up the Australian Government’s Data Analytics Centre of Excellence. From 1991 to 2004 he was Principal Computer Scientist for Data Mining with CSIRO, the Australian government’s premier research organisation. He served as a Senior International Expert and Visiting Professor of the Chinese Academy of Sciences at the Shenzhen Institutes of Advanced Technologies since 2011 and is Adjunct Professor in Data Mining, Fraud Prevention, Security, at the University of Canberra and Australian National University. Graham is an active machine learning researcher and regularly teaches data mining courses and maintains resources for the data scientist. He is author of the Rattle software for data mining and of the Rattle book published in 2011: Data Mining with Rattle and R: The Art of Excavating Knowledge from Data. Graham has been involved in data mining projects for clients from government, finance, banking, insurance, health, environment, and industry for over 30 years.
Graham’s research achievements are in ensemble learning, outlier detection and hot spots discovery. He is involved in numerous international artificial intelligence and data mining research activities and conferences and has edited a number of books and has authored many academic and industry papers. He is past-chair of the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) and co-chair of the Australasian Conference on Data Mining (AusDM).
Graham is a thought leader sharing a vision of the future with machine learning underlying the internet of things, bots, and conversation as a platform, with new approaches to distributed data with the maintenance of privacy. He actively works to make data science readily accessible and available, supporting innovation and the sharing of our knowledge widely through open source software and platforms.