Working across ideas, people, and systems.

Deriving a Generalised Kaleidoscope transform for frequency-based autoencoders and supporting research on frequency-domain methods for computer vision.

• Developing mathematically grounded frequency–spatial transformations for segmentation.
• Contributing to a manuscript targeted for submission in 2026.

Teaching across machine learning, functional programming, theory of computing, and introductory software engineering.

• Delivering classes and guiding students through programming and mathematical concepts.
• Marking assessments and providing practical, constructive feedback.

Leading sponsorship outreach, pitching, negotiation, and partner relationships for one of UQ’s largest technology societies.

• Secured more than $30,000 in sponsorship funding.
• Increased funding by 40% and expanded the sponsor base by 30% in 2026.

Designed a PsychoNet-based decoder and derived Fourier Shuffle for frequency–spatial upsampling in medical image segmentation.

• Improved validation performance by 1.77% against a U-Net baseline.
• Reduced GPU memory usage by 13.8% with custom PyTorch training scripts.

Built AI-assisted systems that summarised financial data and helped brokers complete reports more efficiently and accurately.