Researcher

Keywords

Biography

I am a Lecturer in the School of Mathematics and Statistics at the University of New South Wales. My research sits at the intersection of statistics, machine learning, and environmental science, with a focus on developing methods that help ecologists monitor, assess and protect the natural environment. I work particularly with remote underwater video, ecological abundance data and multivariate study design, aiming to turn complex environmental...view more

I am a Lecturer in the School of Mathematics and Statistics at the University of New South Wales. My research sits at the intersection of statistics, machine learning, and environmental science, with a focus on developing methods that help ecologists monitor, assess and protect the natural environment. I work particularly with remote underwater video, ecological abundance data and multivariate study design, aiming to turn complex environmental data into reliable evidence for biodiversity monitoring and decision-making.

I completed my PhD in Statistics at UNSW and CSIRO Data61 in 2025, where I developed statistical and AI methods for ecological monitoring. This work included movement-aware object detection for underwater video, the assessment of methods used to analyse these videos, and the development of a novel means to estimating fish abundance while accounting for imperfect detection using distance sampling. I have also developed a power-analysis tool to answer study design questions when collecting multivariate ecological data with an acompanying R package ecopower, which has been downloaded more than 14,000 times from CRAN.

Before becoming a Lecturer, I worked for more than six years as a Statistical Consultant at UNSW Stats Central, supporting researchers and Higher Degree Research students with study design, statistical modelling and analysis. Across my research, teaching and consulting, I collaborate with ecologists, environmental scientists, other statisticians and data scientists to develop practical, reproducible methods that contribute to environmental resilience.

 


My Qualifications

PhD in Statistics at UNSW and CSIRO Data61 (2025)


My Awards

  • People's choice award for best presentation at UNSW Mathematics and Statistics Postgraduate Conference - 2024
  • Tjanpi Award for best Student paper in Environmental Statistics (runner up) - 2023
  • 1st place in the School of Mathematics and Statistics in the UNSW 1 minute thesis competition - 2021

My Research Supervision


Areas of supervision

I am not currently supervising any research students, however, if you are interested in one of the below research projects, or have your own research project idea and are looking for a supervisor, feel free to get in touch.

Potential student projects

  • Develop methods to obtain unbiased fish length frequencies from stereo underwater videos using distance sampling to obtain probability of detection per differently sized fish. This will allow more honest depictions of fish size distributions sampled from underwater videos in assessing the health of a fish population.
  • Estimate bias from baiting in underwater videos using simulation, and undergo power analysis of unbaited videos using AI object detection to show unbaited videos analysed through AI can achieve similar power to baited videos which have issues with bias.
  • Develop methods to convert point annotation data from manually analysed videos to large scale object detection algorithms.
  • AI object detection of stereo videos to automate distance sampling abundance estimation.
  • Model uncertainty from AI predictions in subsequent downstream abundance regression models.
  • Combine multiple sources of shark data (e.g. from commercial bycatch, drumline and tagging, human-shark mortality rates, coastal drone observations, Close-Kin Mark-Recapture abundance estimates) in joint mixture models to look at how shark abundance has changed over time.
  • Use deep learning algorithms to produce large scale species distribution models on citizen science data (e.g. ebird or Inaturalist) and use explainable AI (e.g. SHAP values) to gain inference.
  • Partition -diversity (species compositional changes across sites) into ‘nestedness’ & ‘turnover’ components traditionally measured using arbitrary metrics, using multivariate generalised mixed models instead.

My Teaching

Lectured/convened:
•    Civil and Environmental Engineering Computations, (School of Mathematics and Statistics, UNSW) - 2026
•    Introduction to Mixed Models with R (Stats Central, UNSW) - 2026
•    Statistics for Medical Science (Stats Central, UNSW) - 2025-2026
•    Introduction to Regression with R (Stats Central, UNSW) - 2019, 2021-2025
•    Introduction to R (Stats Central, UNSW) - 2019, 2022
•    Introduction to Statistics with R (Stats Central, UNSW) - 2022
•    Ecological statistics (Stats Central, UNSW) - 2018-2019, 2021-2022

Teaching:
•    Higher Probability and Stochastic Processes (School of Mathematics and Statistics, UNSW) - 2018
•    Numerical Methods and Statistics (School of Mathematics and Statistics, UNSW) - 2018
•    Mathematics for Life and Social Sciences (School of Mathematics and Statistics, UNSW) - 2018
 

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Location

School of Mathematics and Statistics University of New South Wales Sydney NSW 2052 Anita B. Lawrence Centre (East) Room 6113 (level 6)

Contact

9385 7111