students

current and previous research supervisions

I’ve been privileged to supervise many students on a variety of interesting (and increasingly interdisciplinary!) topics. They are listed below, along with their research outputs.

Let’s hope I can keep this up to date…!

PhD students (3+ years)

  • Paula Maddigan (July 2024-): “Artificial Intelligence for Smart Conservation”, with Rachael Shaw (Ecology).
  • Sofie Claridge (April 2024-): “Machine Learning for Quench Detection in Superconducting Magnets”, with Dominic Moseley (RRI) and Rod Badcock (RRI).
  • Mayuravaani Mathuranathan (July 2023-): “Machine Learning for Hearing Aid Signal Processing”, with Bastiaan Kleijn.
  • Meeth (Nimasha) Herath (July 2023-): “Harnessing the Power of Deep Learning and Earth Observation for Flood Forecasting in Aotearoa”, with Anya Leenman (Geography) and Mairéad de Róiste (Geography).
  • Ilya Shabanov (April 2023-): “Modelling of Forest Succession”, with Julie Deslippe (Ecology) and Johnathan Tonkin (UoC).
  • Hayden Andersen (March 2020-): “Evolving Human-Friendly Explanations”, with Will Browne (QUT) and Yi Mei.

MSc students (1 year)

  • Johniel Bocacao (April 2024-): “Improving Algorithm Safety Tools for the New Zealand Public Sector”, with Ali Knott.
  • Abigail Clennell (March 2024-): “AI-Driven Kākā Facial Recognition for Conservation”, with Rachael Shaw (Ecology).
  • Michael Stanley (August 2021-August 2022): “Machine Learning for Tarakihi Fish Length Estimation in Aotearoa”, with Mengjie Zhang. Thesis PDF.

MAI/MCompSci project students (1 trimester)

  • Richard Kyle (2023): “Hierarchical Audio-Conditional Image Generation with AudioCLIP Latents”, with Stephen Marsland (Maths). Report PDF.
  • Harsh Panchal (2021): “Identification of Irrigated Land Using Machine Learning Techniques”, with Harith Al-Sahaf. Report PDF.
  • Finn Schofield (2021, sole supervisor): “Genetic Programming Encoder for Autoencoding”. Report PDF.
  • Alex Monckton (2021, sole supervisor): “Unsupervised Outlier Detection using Evolutionary Algorithm Techniques”. Report PDF.

Honours project students (30/45pts)

  • Nathan Bennett (2024, primary): “Enhancing Legal Aid in Aotearoa with Large Language Models”, with Matt Farrington (Legal Services).
  • Georgia Barrand (2024, secondary): “AI-Powered Outfit Selection Application”, with Stuart Marshall.
  • Annie Cho (2024, primary): “Udderly Advanced: AI’s Leap into Milk Analysis”, with Gideon Gouws.
  • Alix Schultze (2024, co-): “A Machine Learning Approach to Binary Equivalence”, with Jens Dietrich.
  • Matthew Edmundson (2023, co-): “A hate speech classifier trained to predict a distribution of ratings”, with Ali Knott. Report PDF.
  • Ethan Maxwell (2023, secondary): “Better, Faster Optimisation”, with Marcus Frean. Report PDF.
  • Tarik (Hasan) Kurnaz (2023, primary): “Discovery of Neural Network Weight Update Equations Through Genetic Programming”, with Marcus Frean. Report PDF.
  • Fintan O’Sullivan (2022, primary): “Feature-based Image Matching for Identifying Individual Kākā”. Report PDF, with Rachael Shaw (Ecology).
  • Luis Slyfield (2022, primary): “Consensus Ascent – Beating Naive Gradient-Based Optimisation”, with Marcus Frean. Report PDF, with Marcus Frean.
  • Jackson Jourdain (2022, secondary): “Automating Glacier Change Monitoring in the Southern Alps of New Zealand”, with Bach Nguyen and Lauren Vargo (ARC). Report PDF.
  • Caitlin Fisher (2022, primary): “A Counterfactual Visualisation System for eXplainable Machine Learning”, with Stuart Marshall and Hayden Andersen. Report PDF.
  • Michael Blayney (2022, primary): “Creating Counterfactuals for Text Analysis (eXplainable AI)”, with Hayden Andersen. Report PDF.
  • Jack Naish (2021, secondary): “How to Train Your Spaceplane”, with Will Browne and Dawn Aerospace. Report PDF unavailable (commerically sensitive).
  • Matt Rothwell (2021, primary): “Automatic Assessment of Image Quality from At-Sea Monitoring Systems”, with Dragonfly Data Science. Report PDF.
  • Michael Behan (2021, primart): “Predicting Public Transport Loadings using a Prediction Model”, with Metlink. Report PDF.
  • Hayden Andersen (2020, primary): “Evolving Clustering Similarity Functions”, with Bing Xue. Report PDF.
  • Damien O’Neill (2018, co-): “PSO for Simultaneous Feature Selection and Weighting in High Dimensional Clustering”, with Bing Xue and Mengjie Zhang. Report PDF.

Directed Individual Study Students (15pts)

  • Oskar Erhardt (2023): “End-to-End Automated Recognition of Individual Kākā”, with Rachael Shaw (Ecology).
  • Asher Stout (2022): “Interpretability Techniques for Explaining Diffusion Probabilistic Models”.
  • Harry Rodger (2021): “Large Language Models for Predicting Assault Sentences”, with Marcin Betkier (Law).
  • Finn Schofield (2021): “Stack-based Genetic Programming for Non-linear Dimensionality Reduction”.

Research Assistants (Paid!)

  • Paula Maddigan (2023): working on the intersection between LLMs and GP, with an explainability lens, with Bing Xue.
  • Benjamin Cravens (2023): GP for Explainable Dimensionality Reduction, with Bing Xue.
  • Asher Stout (2022-2023): working on ML-based automated analysis of milk droplets, with Gideon Gouws and Harith Al-Sahaf.
  • Finn Schofield (2021-2022): worked on GP and NLDR research.

Summer Scholarship Students (1 summer, ~$8k stipend)

  • Benjamin Cravens (2022-2023): Genetic Programming for Explainable Dimensionality Reduction, with Bing Xue.
  • Oskar Erhardt (2022-2023): AI Kākā recognition project, with Rachael Shaw.
  • Fintan O’Sullivan (2021-2022): AI Kākā recognition project, with Rachael Shaw.
  • Luis Slyfield (2021-2022): XAI GP project, with Yi Mei.
  • Hayden Andersen (2020-2021): GP for clustering project, with Bing Xue.
  • Finn Schofield (2019-2020, 2020-2021): GP for clustering and GP for NLDR projects.