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PhD (University of Queensland), PGDipMgmt (Macquarie University), BEng(Hons 1)(Computer Systems) (University of Queensland)
David Lovell is a Professor in the QUT’s School of Computer Science. From 2020-21, David was Deputy Director of QUT’s Centre for Data Science, and leader of the Centre's Data-Focused Decision-Making Program, stepping down in 2022 to recover from injury. David’s research interests lie at the intersection of humanity, science and technology, particularly data science. We humans are the ones who stand to benefit (or suffer) from systems that use data to make or inform decisions that affect our lives. David wants to ensure that science and technology are developed, designed and delivered with this in mind so that our world is better as a result. David joined QUT in December 2014 as Head of the School of Electrical Engineering and Computer Science (EECS). In his five-year term, he provided leadership, support, energy and enthusiasm to EECS in all facets of the School's research, teaching and engagement. He fostered a culture of support and encouragement that enabled the School and its staff to thrive and which has set up QUT’s new School of Electrical Engineering and Robotics, and School of Computer Science for great success from 2020 onwards. David graduated from the University of Queensland's Department of Electrical Engineering in 1989 (BEng Computer Systems, Hons I) and received his PhD there in 1994 for research into artificial neural network methods for handwritten character recognition. He completed postdoctoral research in perinatal risk prediction at Cambridge University before joining CSIRO Mathematical and Information Sciences (CMIS) in 1998. At CSIRO, David was involved in a wide range of research and consulting in the analysis of large and complex datasets, worked as Executive Officer to the CEO from 2001-02, and was a member of CSIRO’s Corporate IT Management team from 2002-04. From 2004, David worked in research management within the quantitative biosciences domain, leading CMIS' Statistical Bioinformatics-Agribusiness Group (2004-07), then its Bio Research Program (2007-08), until mid-2008 appointment as Bioinformatics and Analytics Leader for CSIRO’s Transformational Biology initiative. In mid-2012, and in tandem with his role at CSIRO, David was appointed Director of the Australian Bioinformatics Network, an initiative of CSIRO, EMBL Australia and Bioplatforms Australia which has helped strengthen the Australian bioinformatics community and been instrumental in the formation of ABACBS, the Australian Bioinformatics And Computational Biology Society. He is a strong advocate for the use of compositional data analysis (CoDA) in bioscience so that data carrying information about relative abundances of different components are analysed appropriately. As a leader, manager and academic, David remains convinced that the most challenging and rewarding aspect of collaborative research lies in the people involved. He is committed to creative thinking, delivering benefits from R&D, fostering effective working relationships, and communicating clearly.
Additional information
- Lovell, D., (2021). Log-Ratio Analysis of Finite Precision Data: Caveats, and Connections to Digital Lines and Number Theory. In P. Filzmoser, K. Hron, JA. Martín-Fernández & J. Palarea-Albaladejo (Eds.), Advances in Compositional Data Analysis: Festschrift in Honour of Vera Pawlowsky-Glahn (pp. 45–60). Springer. https://eprints.qut.edu.au/211559
- Roomkham, S., Lovell, D., Cheung, J. & Perrin, D. (2018). Promises and challenges in the use of consumer-grade devices for sleep monitoring. IEEE Reviews in Biomedical Engineering, 11, 53–67. https://eprints.qut.edu.au/120984
- Quinn, T., Richardson, M., Lovell, D. & Crowley, T. (2017). propr: An R-package for identifying proportionally abundant features using compositional data analysis. Scientific Reports, 7, 1–9. https://eprints.qut.edu.au/115564
- Lovell, D., Pawlowsky-Glahn, V., Egozcue, J., Marguerat, S. & Bahler, J. (2015). Proportionality: A valid alternative to correlation for relative data. PLoS Computational Biology, 11(3), 1–12. https://eprints.qut.edu.au/82997
- Nguyen, C., Lovell, D., Adcock, M. & Salle, J. (2014). Capturing natural-colour 3D models of insects for species discovery and diagnostics. PLoS One, 9(4), 1–11. https://eprints.qut.edu.au/79875
- Newman, J., Bolton, E., Mueller-Dieckmann, J., Fazio, V., Gallagher, D., Lovell, D., Luft, J., Peat, T., Ratcliffe, D., Sayle, R., Snell, E., Taylor, K., Vallotton, P., Velanker, S. & von Delft, F. (2012). On the need for an international effort to capture, share and use crystallization screening data. Acta Crystallographica Section F:Structural Biology Communications, 68(3), 253–258. https://eprints.qut.edu.au/79863
- Nguyen, C., Izadi, S. & Lovell, D. (2012). Modeling kinect sensor noise for improved 3D reconstruction and tracking. Proceedings of the 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT 2012), 524–530. https://eprints.qut.edu.au/79864
- Lovell, D., Muller, W., Taylor, J., Zwart, A. & Helliwell, C. (2011). Proportions, percentages, ppm: do the molecular biosciences treat compositional data right? In A. Buccianti & V. Pawlowsky-Glahn (Eds.), Compositional data analysis: Theory and applications (pp. 193–207). John Wiley & Sons. https://eprints.qut.edu.au/79854
- Stone, G., Chapman, B. & Lovell, D. (2009). Development of a log-quadratic model to describe microbial inactivation, illustrated by thermal inactivation of Clostridium botulinum. Applied and Environmental Microbiology, 75(22), 6998–7005. https://eprints.qut.edu.au/79871
- Wynne, J., O'Sullivan, M., Cook, M., Stone, G., Nowak, B., Lovell, D. & Elliott, N. (2008). Transcriptome analyses of amoebic gill disease-affected Atlantic salmon (Salmo salar) tissues reveal localized host gene suppression. Marine Biotechnology, 10(4), 388–403. https://eprints.qut.edu.au/79856