School of Mathematics and Statistics

I am a mathematician with research interests at the intersection of number theory, algebra, and geometry. My work focuses on understanding solutions to systems of polynomial equations and how these solutions behave over different number systems. More broadly, I am interested in the deep connections between arithmetic, geometry, and symmetry, often combining abstract theoretical ideas with explicit computational methods. My research aims to develop new tools for studying longstanding mathematical problems while also advancing computational techniques that can be used across pure mathematics and its application to fields such as cryptography.

I am a Lecturer in the School of Mathematics and Statistics specialising in machine learning, data science, and artificial intelligence (AI). I obtained a PhD in Computer Science from the University of Auckland, focusing on robustness, fairness, and bias mitigation in machine learning. I subsequently held a prestigious Marie Skłodowska-Curie Fellowship at the Jožef Stefan Institute in Slovenia, where I led research on active learning for accelerated scientific discovery, particularly in materials science and life sciences. Alongside my academic career, I have also worked as a Data Scientist in industry at DB Fernverkehr AG (German national train company), bringing practical experience of deploying data-driven and AI-based methods to real-world problems.

My research centers on computational and applied statistics, with a strong emphasis on modern estimation techniques and uncertainty quantification. I am particularly interested in hierarchical and nonlinear modelling, model selection, and the translation of statistical methodology into robust, user-friendly software tools.

Alongside my research, my work as a statistical consultant is grounded in rigorous methodology and thoughtful computational implementation, ensuring that advanced statistical techniques are both practically applicable and scientifically sound.

While originally motivated by applications in genomics, specifically as applied to livestock species such as sheep, my research has expanded to include

• Investigating properties of linear mixed models, particularly around random effect prediction in very large datasets.
• Probabilistic approaches to determining latent structure, inspired by classical dimension reduction techniques such as principal components/factor  analysis, for a variety of categorical data types found in the social and health sciences, with examples ranging from arrest and prosecution data, and to simultaneous modelling of cause of death and age of death time series.
• Approximate Bayesian inference, mainly Variational Bayesian methods, with a focus on logistic regression (but can cover negative binomial) type models for a range of priors. This also includes  studies of the properties of logistic transformations of normal random variables, and the possibility of developing more accurate confidence/credible intervals for proportions and odds ratios. 

My research interests focus on mathematical modelling, taking real life problems and data and using mathematics to explore phenomenology and underlying principles. I work in arange of areas including ecology, social sciences, eduction and epidemiology.

Ngin-Tee Koh was grew up in the sunny island of Singapore known as the little red dot, where he got his undergraduate degree with first class honours.  Then he earned his PhD in the United States at the University of Illinois (Urbana-Champaign) under the direction of Aimo Hinkkanen.  Prior to his undergraduate studies, he had undergone two and half years of military service in Singapore.

Outside the classroom, Ngin-Tee Koh regularly applies biomechanics to the fast-paced sports of badminton and squash with a number of his five children.  He is fluent in both English and Mandarin.

The focus of my research is in developing methods for modelling and inference in spatial point patterns. I’m particularly interested in parameter estimation for clustered processes, approximate and pseudo likelihood methods, and kernel smoothing.

My research centres on the rigorous application of state-of-the-art statistical methodology, with particular expertise in Bayesian inference and spatio-temporal modelling. I am passionate about ensuring that advanced methods are implemented correctly and meaningfully across disciplines, so that statistical analysis translates into genuine community impact. My work spans environmental epidemiology, biological sciences, soil science, and public health, where complex data structures demand careful modelling and principled uncertainty quantification.

Professor Clemency Montelle is Head of the School of Mathematics and Statistics at Te Whare Wānanga o Waitaha | University of Canterbury and an internationally recognised scholar in the history and philosophy of mathematics. In 2026 she became the first person outside the United Kingdom to receive the prestigious Agnes Mary Clerke Medal from the Royal Astronomical Society, recognising her outstanding contributions to the history of astronomy and its mathematical traditions.

With expertise in several ancient languages, she studies how early cultures developed sophisticated mathematical and astronomical knowledge, revealing the global foundations of modern science. Her work combines mathematical insight with innovative digital tools to analyse historical data, bringing new life to centuries-old scientific texts and highlighting the enduring relevance of mathematical thinking across time and cultures. As a research leader, she champions interdisciplinary collaboration and is committed to strengthening research excellence while broadening public understanding of the role mathematics plays in shaping contemporary data-driven disciplines and societal impact.

Michael Plank is a Professor in the School of Mathematics and Statistics at the University of Canterbury, a Fellow of the Royal Society Te Apārangi, and an Investigator at Te Pūnaha Matatini, New Zealand's Centre of Research Excellence in Complex Systems and Data Analytics. He obtained a BSc(Hons) in Mathematics from the University of Bristol in 2000 and a PhD in Applied Mathematics from the University of Leeds in 2003. He started at the University of Canterbury as a postdoctoral research fellow in 2004 and as a continuing academic staff member from 2006. His research interests are in infectious disease dynamics and mathematical biology and he has interdisciplinary collaborations with epidemiologists, biologists, clinicians and policymakers.