QUT - Academic profiles - Dr Timothy Chappell

After completing his PhD at Queensland University of Technology in 2015, he worked as a Research Associate at QUT until 2017.

He is currently a Lecturer in the School of Computer Science at QUT.

His research is centred around finding efficient ways to store, search, cluster and learn from large-scale data sets, usually text, images or biological sequences.

0731389111

timothy.chappell@qut.edu.au

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QUT alumni

Personal details

Positions

Lecturer (TIEA)
Faculty of Science,
School of Computer Science

Qualifications

PhD(DataScience) (Queensland University of Technology)
Bachelor of Information Technology (Honours) (Queensland University of Technology)

Teaching

Current units:

CAB302: Software Development (unit coordinator)
CAB403: Systems Programming (unit coordinator)

Past units:

Experience

In 2018 he was part of a group at QUT that developed change detection algorithms and software as part of a project funded by the Queensland Government's Department of Natural Resources, Mines and Energy. He also developed the initial change detection tool prototype delivered as part of the proposal.

Publications

QUT ePrints

For more publications by Timothy, explore their research in QUT ePrints (our digital repository).

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2024

Li, L., Woodley, A. & Chappell, T. (2024). Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms. Sustainability, Article 2493, 16(6). https://eprints.qut.edu.au/247391

2022

Bradford, J., Chappell, T. & Perrin, D. (2022). Rapid Whole-Genome Identification of High Quality CRISPR Guide RNAs with the Crackling Method. CRISPR Journal, 5(3), 410–421. https://eprints.qut.edu.au/234989
Chappell, T., Geva, S., Hogan, J. M., Lovell, D., Trotman, A. & Perrin, D. (2022). Metagenomic Geolocation Using Read Signatures. Frontiers in Genetics, Article 643592, 13. https://eprints.qut.edu.au/234990

2020

McLaughlin, C., Woodley, A., Geva, S., Chappell, T., Kelly, W., Boles, W., De Vine, L. & Hutson, H. (2020). Object Based Remote Sensing Using Sentinel Data. Proceedings of the 2020 Digital Image Computing: Techniques and Applications (DICTA 2020), Institute of Electrical and Electronics Engineers Inc.. https://eprints.qut.edu.au/227780

2019

Woodley, A., McLaughlin, C., Hutson, H., Geva, S., Chappell, T., Kelly, W., Perrin, D., Boles, W. & De Vine, L. (2019). High resolution change detection using Planet mosaic. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 6578–6581. Institute of Electrical and Electronics Engineers Inc.. https://eprints.qut.edu.au/197029
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
McLaughlin, C., Hutson, H., De Vine, L., Woodley, A., Geva, S., Chappell, T., Kelly, W., Boles, W. & Perrin, D. I. (2019). Change detection over the State of Queensland using high resolution planet satellite mosaics. Proceedings of the 2019 Digital Image Computing: Techniques and Applications (DICTA), 1–8. Institute of Electrical and Electronics Engineers Inc.. https://eprints.qut.edu.au/197701

2018

Chappell, T., Geva, S., Hogan, J. & Perrin, D. (2018). Rapid hierarchical clustering of biological sequences. Proceedings of the 23rd Australasian Document Computing Symposium, 1–4. Association for Computing Machinery. https://eprints.qut.edu.au/126085
Chappell, T., Geva, S., Hogan, J., Huygens, F., Rathnayake, I., Rudd, S., Kelly, W. & Perrin, D. (2018). Rapid analysis of metagenomic data using signature-based clustering. BMC Bioinformatics, Article 509, 19(S20), 79–93. https://eprints.qut.edu.au/129501

2017

Woodley, A., Chappell, T., Geva, S. & Nayak, R. (2017). Using web services to fuse remote sensing and multimedia data repositories. Proceedings of the Australasian Computer Science Week Multiconference 2017, 1–8. Association for Computing Machinery. https://eprints.qut.edu.au/105644
Chappell, T., Cifuentes, C., Krishnan, P. & Geva, S. (2017). Machine learning for finding bugs: An initial report. Proceedings of the 2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE), 21–26. Institute of Electrical and Electronics Engineers Inc.. https://eprints.qut.edu.au/105982
Chappell, T., Geva, S., Hogan, J., Huygens, F., Kelly, W. & Perrin, D. (2017). Signature-based clustering for analysis of the wound microbiome. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 339–346. Institute of Electrical and Electronics Engineers Inc.. https://eprints.qut.edu.au/115911
Chappell, T., Geva, S. & Hogan, J. (2017). K-means clustering of biological sequences. Proceedings of the 22nd Australasian Document Computing Symposium, 1–4. Association for Computing Machinery. https://eprints.qut.edu.au/116844
Buckingham, L., Chappell, T., Hogan, J. & Geva, S. (2017). Similarity projection: A geometric measure for comparison of biological sequences. Proceedings of the 2017 IEEE 13th International Conference on e-Science (e-Science), 325–334. Institute of Electrical and Electronics Engineers (IEEE). https://eprints.qut.edu.au/116841

2016

Nanayakkara Wasam Uluwitige, D. C., Chappell, T., Geva, S. & Chandran, V. (2016). Improving retrieval quality using pseudo relevance feedback in content-based image retrieval. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 873–876. Association for Computing Machinery. https://eprints.qut.edu.au/95404

A complete list of publications is available at: https://www.qut.edu.au/about/our-people/academic-profiles/timothy.chappell

Supervision

Supervision topics

Information retrieval and coding methods for large scale bioinformatics

Dr Timothy Chappell

Biography