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)

  1. Tan Nguyen (February 2026-): TBD.
  2. Nicolas Samelson (October 2025-): "AI Re-Identification of Kākā", with Junhong (Jennifer) Zhao and Rachael Shaw (Ecology).
  3. Fraser Campbell (September 2025-): "Behaviour and Ecology of Urban Kākā.", with Rachael Shaw (Ecology).
  4. Alix Schultze (February 2025-): "An Explainable Machine Learning Approach to Binary Equivalence", with Jens Dietrich.
  5. Sofie Claridge (April 2024-): "Machine Learning for Enhanced Temperature Sensing of Optical Fibres", with Dominic Moseley (RRI) and Rod Badcock (RRI).
  6. Mayuravaani Mathuranathan (July 2023-): "Enhanced Voice Analysis and Processing for Hearing Aids", with Bastiaan Kleijn.
  7. 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) and Emily O'Riordan.
  8. Ilya Shabanov (April 2023-): "Modelling of Forest Succession", with Julie Deslippe (Ecology) and Jonathan Tonkin (UoC).
  9. Hayden Andersen (March 2020-): "Evolving Human-Friendly Explanations", with Will Browne (QUT) and Yi Mei.

MSc students (1 year)

  1. Ben Cravens (2025-2027): "Preparing for Advanced Artificial Intelligence: Scenarios, Risks, and Policy Responses", with Ali Knott.
  2. Ryan Jaggers (2025-2026): "Understanding Urban Kākā Ecology through AI", with Rachael Shaw (Ecology).
  3. Marie Katsanos (March 2025-2026): "The Impact of Generative AI on Creativity.", with Bart Ellenbroek (Psychology).
  4. Johniel Bocacao (April 2024-2026, PT): "Unified Guidance Is All You Need - Improving Safe AI System Delivery in the NZ Government", with Ali Knott.
  5. Abigail Clennell (March 2024-): "AI-Driven Kākā Facial Recognition for Conservation", with Rachael Shaw (Ecology).
  6. 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)

  1. Qaswar Almousa (2025): "Integrating Computer Vision, Explainable AI, and MLOps for Predator Detection in Conservation Contexts".
  2. Alfonso Martinez (2024): "Patterns in Fear-Inducing Experiences", with David Carmel et al. (Psychology).
  3. Richard Kyle (2023): "Hierarchical Audio-Conditional Image Generation with AudioCLIP Latents", with Stephen Marsland (Maths). Report PDF.
  4. Harsh Panchal (2021): "Identification of Irrigated Land Using Machine Learning Techniques", with Harith Al-Sahaf. Report PDF.
  5. Finn Schofield (2021, sole supervisor): "Genetic Programming Encoder for Autoencoding". Report PDF.
  6. Alex Monckton (2021, sole supervisor): "Unsupervised Outlier Detection using Evolutionary
    Algorithm Techniques    ". Report PDF.

Honours project students (30/45pts)

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

  1. Arnav Dogra (2025): "Understanding RAG and MCP in Modular AI Systems".
  2. Paula Maddigan (2024-2025): "Artificial Intelligence for Smart Conservation", with Rachael Shaw (Ecology).
  3. Louis Isbister (2024): "What Makes A Good Marsden?", with Stephen Skalicky (Lingustics) and the VUW Research Office.
  4. Thomas Walker (2024): "How Will AGI Change How We Think and Act?".
  5. Oskar Erhardt (2023): "End-to-End Automated Recognition of Individual Kākā", with Rachael Shaw (Ecology).
  6. Asher Stout (2022): "Interpretability Techniques for Explaining Diffusion Probabilistic Models".
  7. Harry Rodger (2021): "Large Language Models for Predicting Assault Sentences", with Marcin Betkier (Law).
  8. Finn Schofield (2021): "Stack-based Genetic Programming for Non-linear Dimensionality Reduction".

Research Assistants

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

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

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