Dr Graham J Williams
(A Longer Bio is available.)
Dr Graham Williams is Director of Data Science, Cloud AI, Microsoft Asia. He has a PhD in Machine Learning and is an Artificial Intelligence researcher, practitioner, and educator with over 30 years in the industry. He has authored many books and papers as well as a number of popular software packages including Rattle for data mining. He is the author of the book Data Mining with Rattle and R: The Art of Excavating Knowledge from Data published in 2011 and The Essentials of Data Science: Knowledge Discovery Using R published in 2017.
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 Australia. He served as a Senior International Expert and Visiting Professor of the Chinese Academy of Sciences since 2011 and 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 courses and maintains resources for the data scientist.
Graham’s research has pioneered developments in ensemble learning, outlier detection and profile 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’s current research encompasses machine learning at the edge, driving the discovery of knowledge from massive compute over large data through personal edge devices. Knowledge is captured and represented transparently to drive intelligent reasoning over those knowledge structures. This is technology that underlies the internet of things particularly for massively distributed data and compute operating in a privacy preserved environment.
Graham is also active in ensuring machine learning and artificial intelligence technology is readily accessible and available to everyone through open source software and platforms primarily in R and Python.