Research Scientist in Data Analysis Method Development
My current main area of research is in developing methods for combining different probabilistic estimates, primarily from various geophysics, to produce bigger picture insights and estimates to aid mineral exploration. Other past work includes development of automated sports prediction systems for industry. As an undergraduate, I studied computer science and mathematics. As a postgraduate my research focused on contributing to the theoretical foundations of Bayesian methods.
Throughout my career and studies I have focused on developing the ability to quickly step into new fields and engage with experts from diverse disciplines. Fields I have worked with include climatology, image analysis, sports science and also geophysics. I take great interest not only in the mechanics of statistical and data science methods, but also how the are used and abused in practice.
- Deep knowledge of probabilistic method implementation and theory
- Understanding of geophysical inversion methods
- Strong mathematical background
- Experienced programmer familiar with good coding practices and a wide variety of tools and languages
- Good presentation and instruction skills from years spent teaching
May 2017 – present
Postdoctoral fellow: CSIRO Deep Earth Imaging Future Science Platform
Data modeller/programmer: Infoplum
Research assistant: Monash University
PhD (Computer science/mathematical statistics) Monash University Australia (2017)
BSc (Hons, Computer Science) Monash University Australia (2007)
BSc (Computer Science) University of Melbourne. Australia (2006)
Visser, G. (2019). Smart stitching: adding lateral priors to ensemble inversions as a post-processing step. ASEG Extended Abstracts, 2019(1), 1-4.
Visser, G., Guo, P., and Saygin, E. (2019). Bayesian transdimensional seismic full-waveform inversion with a dipping layer parameterization. Geophysics, 84(6), R845-R858.
Visser, G., and Markov, J. (2019). Cover thickness uncertainty mapping using Bayesian estimate fusion: leveraging domain knowledge. Geophysical Journal International 219(3), 1474-1490.
Visser, G. (2016). Interest-relative induction. Ph.D. thesis, Monash University.
Visser, G., Dale, P., Dowe, DL., Ndoen, E., Dale, M., and Sipe, N. (2012). A novel approach for modeling malaria incidence using complex categorical household data: the minimum message length (MML) method applied to Indonesian data. Computational Ecology and Software, 2.
Visser, G., Dowe, DL, and Uotila, P. (2009). Enhancing MML clustering using context data with climate applications. Springer Lecture Notes in Artificial intelligence (LNAI) and Australasian Conference on Artificial Intelligence, 350–359.
Visser, G., Dowe, DL, and Svalbe, ID. (2009). Information-theoretic image reconstruction and segmentation from noisy projections. In: Springer Lecture Notes in Artificial intelligence(LNAI) and Australasian Conference on Artificial Intelligence, 170–179.
Visser, G., and Dowe, DL. (2007). Minimum message length clustering of spatially-correlated data with varying inter-class penalties. Proc. 6th IEEE International Conference on Computer and Information Science (ICIS 2007), 17–22.