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PhD (Queensland University of Technology)
Dr. Alan Woodley is the leader of the Spatial Informatics Laboratory and a Senior Lecturer in the School of Computer Science. He has a background in data and computer science with a Bachelor of Information Technology and a Ph.D. from QUT. Research His research has focussed on applying efficient ‘big data’ solutions covering data analytics, computational modeling, and visualisation. His work has been applied to the mining and environmental domains and has developed research frameworks and tools to improve industrial sustainability and to bridge the social-technical divide. He has obtained research funding of over 3 million dollars and published over 60 articles. Education
- Massachusetts Institute of Technology, Certificate in New Ventures Leadership, MIT Innovation and Entrepreneurship Bootcamp, 2018
- Queensland University of Technology, Doctor of Philosophy, 2008
- Queensland University of Technology, Bachelor of Information Technology (Hons), 2003
- Queensland University of Technology, Bachelor of Information Technology, 2003
Experience
- Senior Lecturer, Queensland University of Technology, 2018 - Current
- Vice Chancellor’s Research Fellow, Queensland University of Technology, 2018 - 2019
- Lecturer, Queensland University of Technology, 2017
- Research Fellow, Queensland University of Technology, 2014 - 2016
- Postdoctoral Research Fellow, Sustainable Minerals Institute, The University of Queensland, 2008 - 2014
Current Roles
- Senior Lecturer, School of Computer Science, Faculty of Science
- Associate Investigator, Centre for Agriculture and the Bioeconomy
- Associate Investigator, Australian Data Science Centre
- Associate Investigator, ARC Centre of Excellence for Mathematical & Statistical Frontiers
Additional information
Dr. Woodley has spent over the past decade working with industry, Some of his success stories include working with:
- The Yarrabilba community, Lendlease and Food Agility CRC to produce a community compost application.
- Working with the Bonneville Power Association, a subsidiary of the US Department of Energy, to produce a salmon monitoring tool.
- The Queensland government and Frontier-SI to produce a high-dimensional land change application.
- Represents of Queensland, New South Wales, Victoria and Western Australia to work with data analytics of remote sensing algorithms.
- Working with representatives of resource industry to help with their water issues.
- Type
- Funding Award
- Reference year
- 2021
- Details
- Awarded $584 from the Food Agility CRC for the development of a Circular Food Economy Digital Framework. Our goal is to build an app for them.
- Type
- Funding Award
- Reference year
- 2021
- Details
- Funding of $149K from the Bonneville Power Administration (a subsidiary of the US Department of Energy) for the project "Analysis of Spatial Stream Networks for Salmonids: Phase 2".This project required us to develop an open source version of our system.
- Type
- Fellowships
- Reference year
- 2018
- Details
- Awarded a prestigious 3-year Vice Chancellor's Research Fellowship in Digitial Agriculture. The Fellowship will join together farm sensors, computational models, and visualizations. The Fellowship will explore how uncertainty is passed from sensors to models and how models can better deal with streaming data.
- Type
- Funding Award
- Reference year
- 2017
- Details
- Funding of $200K from the CRC for Spatial Information (Category 4: Australian Competitive Grant) for the project "Change Detection System from High-Resolution Satellite Images (CDS)". The project uses high resolution (
- Han Onn, A. & Woodley, A. (2014). A discourse analysis on how the sustainability agenda is defined within the mining industry. Journal of Cleaner Production, 84, 116–127. https://eprints.qut.edu.au/75924
- Woodley, A., Tang, E., Geva, S., Nayak, R. & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333–345. https://eprints.qut.edu.au/122363
- Woodley, A., Tang, E., Geva, S., Nayak, R. & Chappell, T. (2016). Using parallel hierarchical clustering to address spatial big data challenges. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), 2692–2698. https://eprints.qut.edu.au/104717
- Woodley, A., Chappell, T., Geva, S. & Nayak, R. (2016). Efficient feature selection and nearest neighbour search for hyperspectral image classification. Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 193–200. https://eprints.qut.edu.au/104530
- Danoucaras, N., Woodley, A. & Moran, C. (2014). The robustness of mine water accounting over a range of operating contexts and commodities. Journal of Cleaner Production, 84, 727–735. https://eprints.qut.edu.au/75928
- Jony, R., Woodley, A. & Perrin, D. (2020). Fusing Visual Features and Metadata to Detect Flooding in Flickr Images. Proceedings of 22nd Digital Image Computing: Technqiues and Applications (DICTA). https://eprints.qut.edu.au/209913
- McIntyre, N., Woodley, A., Danoucaras, N. & Coles, N. (2015). Water management capacity building to support rapidly developing mining economies. Water Policy, 17(6), 1191–1208. https://eprints.qut.edu.au/95174
- Paragreen, N. & Woodley, A. (2013). Social licence to operate and the coal seam gas industry: What can be learnt from already established mining operations? Rural Society, 23(1), 46–59. https://eprints.qut.edu.au/75046
- Baxter, P., Woodley, A. & Hamilton, G. (2017). Modelling the spatial spread risk of plant pests and pathogens for strategic management decisions. Proceedings of the 22nd International Congress on Modelling and Simulation (MODSIM2017), 209–215. https://eprints.qut.edu.au/115051
- Sarker, C., Mejias Alvarez, L., Maire, F. & Woodley, A. (2019). Flood mapping with convolutional neural networks using spatio-contextual pixel information. Remote Sensing, 11(19), 1–25. https://eprints.qut.edu.au/133459