Rate My Professor Matthew Parry

Associate Professor Matthew Parry is an Associate Professor in the Department of Mathematics and Statistics within the Division of Sciences at the University of Otago. He holds a PhD in theoretical physics from Brown University, where his doctoral research focused on cosmology. Parry transitioned into statistics through an EPSRC Statistics Mobility Fellowship in the United Kingdom and has been a member of the Department of Mathematics and Statistics at the University of Otago since 2011. His research expertise lies in mathematical and statistical modelling, with current focus areas including applications of scoring rules for evaluating probabilistic forecasts, modelling epidemics in plant and human populations, statistical analysis of solar storms and geomagnetic events, analysis of gravitational waves, modelling of methylation and epigenetic modifications, and development of new algorithms for statistical inference. These interests are informed by his background in physics and computational methods, spanning decision theory, epidemiology, bioinformatics, cosmology, and astrostatistics.

Parry serves as a principal investigator at Te Pūnaha Matatini Centre of Research Excellence, where he acted as the outgoing lead of the Complexity Community of Inquiry. He is deputy leader of the New Zealand Science Group, part of the Laser Interferometer Space Antenna (LISA) Consortium, and was President of the New Zealand Statistical Association from 2020 to 2022. Key publications include 'Proper local scoring rules' (The Annals of Statistics, 2012), 'Digital contact tracing technologies in epidemics: a rapid review' (Cochrane Database of Systematic Reviews, 2020), 'Bayesian inference for an emerging arboreal epidemic in the presence of control' (Proceedings of the National Academy of Sciences, 2014), 'Sensitivity of reverse transcription polymerase chain reaction tests for severe acute respiratory syndrome coronavirus 2 through time' (The Journal of Infectious Diseases, 2023), and 'How to evaluate probabilistic prediction models: Key metrics' (Journal of Clinical Epidemiology, 2026). His scholarly output has received over 1,765 citations. Parry teaches courses such as STAT 312 Modelling High Dimensional Data, STAT 372 Stochastic Modelling, STAT 405 Probability and Random Processes, and INFO 420 Statistical Techniques for Data Science, and he coordinates the BAppSc in Data Science.