Course co‑ordinator for BAppSc in Data Science
My PhD was in physics but I moved into statistics courtesy of an EPSRC (UK) Statistics Mobility Fellowship. I have been a member of the Department of Mathematics and Statistics since 2011.
I am currently a principal investigator at Te Pūnaha Matatini Centre for Research Excellence. I am deputy leader of NZ Science Group, which is part of the Laser Interferometer Space Antenna (LISA) Consortium. I am also a past President of the NZ Statistical Association (2020–2022).
My teaching responsibilities include:
- STAT 312 Modelling High Dimensional Data
- STAT 372 Stochastic Modelling
- STAT 405 Probability and Random Processes
- INFO 420 Statistical Techniques for Data Science
My research expertise is mathematical and statistical modelling. My research is currently focused on applications of scoring rules in statistics, modelling epidemics in plants and human populations, analysis of solar storms and geomagnetic events, statistical analysis of gravitational waves, modelling of methylation and epigenetic modification, and the development of new algorithms for statistical inference. These activities are strongly influenced by my background in physics and computational methods.
I am actively looking for Honours and postgraduate students to work on projects in these areas. I value mathematical and computational ability as much as I do statistical background.
Nanayakkara, S., Zeng, J., Turner, R., Parry, M., & Sywak, M. (2023). Development and internal validation of clinical risk prediction models for structural recurrence disease of thyroid cancer. Proceedings of the New Zealand Biostatistics Conference. Retrieved from https://events.otago.ac.nz/2023-biostatistics-conference
Yassi, M., Chatterjee, A., & Parry, M. (2023). Application of deep learning in cancer epigenetics through DNA methylation analysis. Briefings in Bioinformatics, 24(6). doi: 10.1093/bib/bbad411
Richter, M. E., Leonard, G. H., Smith, I. J., Langhorne, P. J., & Parry, M. (2023). Interannual variability of fast-ice thickness in McMurdo Sound: Drivers and trends. Proceedings of the New Zealand-Australia Antarctic Science Conference (NZAASC): Latitudes of Change. (pp. 84). Retrieved from https://www.nzaasc.org
Fletcher, D., Dillingham, P. W., & Parry, M. (2023). A simple and robust approach to Bayesian modelling of overdispersed data. Environmental & Ecological Statistics, 30, 289-308. doi: 10.1007/s10651-023-00567-6
Richter, M. E., Leonard, G. H., Smith, I. J., Langhorne, P. J., Mahoney, A. R., & Parry, M. (2022). Accuracy and precision when deriving sea-ice thickness from thermistor strings: A comparison of methods. Journal of Glaciology. Advance online publication. doi: 10.1017/jog.2022.108