projects and talks


Statistical Society of Australia Venables Award Seminar: predictNMB (2023-08-24)

predictNMB was awarded runner-up for the annual Venables award (open source software for data analytics). I was awarded $1000 and gave a talk on the predictNMB for the Statistical Society of Australia.

Presentation repo and live slides

R-Medicine 2023 - predictNMB: An R Package To Estimate if or When a Clinical Prediction Model Is Worthwhile (2023-06-08)

Presented on predictNMB R package developed as part of PhD work. 1-hour demo format with a short presentation followed by live coding.

Code, live slides, and video (eventually)

Statistical Society of Australia QLD Branch Meeting: Considering patient outcomes and healthcare costs when obtaining prediction model cutpoints may improve value-based care (2022-08-03)

Presented on recent PhD work regarding both cutpoint selection (including work presented at Impact Makers) and a new wrapper R package (predictNMB) to simulate clinical prediction models, evaluating their net monetary benefit.

Code and live slides

Impact Makers: Cost effectiveness-informed cutpoints for clinical prediction models (2022-07-15)

Presentation on recent PhD work where I propose a cutpoint selection method that maximises the net monetary benefit of a clinical prediction model.

Code and live slides

Using shiny apps for effectively communicating research results to a broad audiencence (2022-06-17)

Presentation to the Center For Data Science at QUT about the basics of shiny app development and how they can be useful to researchers.



I like working with health-related data and statistical programming. Fortunately, I’ve been able to work with a bunch of great people and across and range of interesting projects.

iTRAQI: injury Treatment & Rehabilitation Accessibility Queensland Index

iTRAQI (injury Treatment and Rehabilitation Accessibility Queensland Index) is a collaborative project involving Jamieson Trauma Institute (JTI), Queensland University of Technology (QUT) and representatives from Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ), supported by the Australian Research Council through the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and QUT’s Centre for Data Science, with additional input from government, community members and clinicians.

I worked on this project as a Senior Research Assistant, performing the analyses and shiny app development.

shiny app code

Circadian rhythms

My honours project was interested in circadian rhythms and neuroanatomy. I ended up getting more interested in what methods we could use to compare circadian characteristics between two groups. This led to the development of CircaCompare (published in Bioinformatics), which is accompanied by an R package, a shiny app, and an implementation in python.

I’ve been fortunate enough for this work to get some interest adn uptake. Since then, I’ve collaborated with groups at The University of Western Australia1, University of Lübeck2, Maastricht University3, and University of Saskatchewan4 in a range of different biomedical research projects.

Recently, I developed another R package (GLMMcosinor) which fits a cosinor model using the glmmTMB framework to extend it’s flexibility to GLMMs5.