Professor Graham Williams
Chief Scientist, Software Innovation Institute
ANU College of Engineering, Computing and Cybernetics
Areas of expertise
- Pattern Recognition And Data Mining 080109
- Artificial Intelligence And Image Processing 0801
Research interests
If interested in a PhD programme please share a demonstration of your work as published, for example, on a git repository. An mlhub package would be a great demonstration of your capabiities.
- Democratising AI, Machine Learning, and Data Science (https://mlhub.ai);
- Privacy and Data Analysis over massively distributed data (https://ecosysl.net);
- Reasoning over Knowledge Graphs, entity resolution and link discovery;
- Open source software for data science (https://rattle.togaware.com);
- New Approaches to Teaching Data Science - Teaching Hospital
Biography
Graham leads teams of developers and researchers delivering innovative and cutting edge Data Science solutions utilising AI and Machine Learning to target important business use cases. He has a focus on developing technology for good and sharing through open source software practices.
Graham served in a number of senior roles across different sectors. With Microsoft Graham was Director of Data Science, mentoring developers and researchers and working across industries. As an executive with the Australian Taxation Office and the Whole of Government Data Analytics Centre of Excellence he guided government departments in setting up their Data Science capabilities. At the Commonwealth Scientific and Industrial Research Organisation (CSIRO) he initiated the first Data Mining research group in Australia. He is the author of books and open source software widely used in teaching.
Graham has a PhD in Computer Science (Machine Learning) from the ANU and BSc Hons (Maths) (First Class) University of Adelaide.
Publications
- Field, E, Dyda, A, Hewett, M et al. 2021, 'Development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), Australia', Frontiers in Public Health, vol. 9, pp. 1-12.
- Broad, E, Sheel, M, Lazar, S et al. 2020, Starting With SOAP: rapid deployment of contract tracing in a pandemic.
- Zhao, H, Williams, G & Huang, J 2017, 'Wsrf: An R package for classification with scalable weighted subspace random forests', Journal of Statistical Software, vol. 77, no. 3, pp. 1-30.
- Zhao, H, Chen, X, Nguyen, T et al 2016, 'Stratified over-sampling bagging method for random forests on imbalanced data', Lecture Notes in Computer Science (LNCS), vol. 9650, pp. 63-72.
- Khan, I, Huang, J, Tung, N et al 2014, 'Ensemble Clustering of High Dimensional Data with FastMap Projection', International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service, ed. W-C Peng, H Wang, J Bailey, V S Tseng, T B Ho, Z-H Zhou and A L P Chen, Springer International Publishing, Switzerland, pp. 483-493pp.
- Tung, N, Huang, J, Khan, I et al 2014, 'Extensions to quantile regression forests for very high-dimensional data', 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014, ed. V S Tseng, T B Ho, Z-H Zhou, A L P Chen, H-Y Kao, Springer Verlag, Tainan China, pp. 247-258.
- Cao, L, Yu, P, Motoda, H et al 2013, 'Special issue on behavior computing', Knowledge and Information Systems, vol. 37, no. 2, pp. 245-249.
- Denny, D, Christen, P & Williams, G 2012, 'Analysis of Cluster Migrations Using Self-Organizing Maps', Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), Conference Organising Committee, Shenzhen China, pp. 171-182.
- Zhao, Z, Feng, S, Wang, Q et al 2012, 'Topic oriented community detection through social objects and link analysis in social networks', Knowledge-Based Systems, vol. 26, pp. 164-173.
- Xu, B, Huang, J, Williams, G et al 2012, 'Hybrid Random Forests: Advantages of Mixed Trees in Classifying Text Data', Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2012), Conference Organising Committee, KL, pp. 147-158.
- Xu, B, Huang, J, Williams, G et al 2012, 'Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces', International Journal of Data Warehousing and mining, vol. 8, no. 2, pp. 44-63.
- Williams, G, Baxter, R, He, H et al. 2002, 'A comparative study of RNN for outlier detection in data mining', IEEE International Conference on Data Mining (ICDM 2002), ed. V. Kumar, S. Tsumoto, N. Zhong, P.S. Yu, X. Wu, IEEE, California, pp. 709-712.
- Williams, G 2009, 'Rattle: A Data Mining GUI for R', The R Journal, vol. 1, no. 2, pp. 45-55.
- Guazzelli, A, Zeller, M, Lin, W et al 2009, 'PMML: An Open Standard for Sharing Models', The R Journal, vol. 1, no. 1, pp. 60-65.
- Denny, D, Williams, G & Christen, P 2008, 'Exploratory Hot Spot Profile Analysis Using Interactive Visual Drill-Down Self-Organizing Maps', Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008), ed. Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi, Springer, New York, pp. 536-543.
- Denny, D, Williams, G & Christen, P 2008, 'ReDSOM: Relative Density Visualization of Temporal Changes in Cluster Structures Using Self-organizing Maps', IEEE International Conference on Data Mining (ICDM 2008), ed. F. Giannotti, D. Gunopulos, F. Turini C. Zaniolo, N. Ramakrishnan, X. Wu, IEEE Computer Society, Los Alamitos, California, pp. 173-182.
- Jin, H, Chen, J, He, H et al. 2008, 'Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions.', IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 4, pp. 488-500.
- Denny, D, Williams, G & Christen, P 2007, 'Exploratory Multilevel Hot Spot Analysis: Australian Taxation Office Case Study', Conferences in Research and Practice in Information Technology - CRPIT, vol. 70, pp. 73-80.
- Williams, G & Simoff, S, eds, 2006, Data mining: theory, methodology, techniques and applications, Springer, Berlin.
- Chen, J, Jin, H, He, H et al. 2006, 'Frequency-based Rate Events in Mining in Administrative Health Data', Electronic Journal of Health Informatics, vol. 1, no. 1, pp. 1-11.
