About Me
Bachelor of Advanced Computing student at the University of Sydney and Dalyell Scholar, interested in machine learning, computer vision, and applied research.
Background
Bachelor of Advanced Computing (2025β2028)
Dalyell Scholar
NCSS 2024
Skills
- Python, Java, JavaScript, SQL
- Machine Learning
- Computer Vision
- Backend Development
- HTML & CSS
Projects
Stellar Property Prediction (GAIA DR2)
Machine learning regression models predicting stellar brightness and properties with 99.6% accuracy.
Alexa Horizon β Space Voice Assistant
Voice-first AI assistant integrating live space data, NLP, and serverless backend.
MatchPoint β Biomechanics Tennis Platform
Computer vision system analysing biomechanics to personalise tennis coaching.
Sydney Urban Cooling
Simulation-based web app showing how tree canopy reduces urban heat.
Sharks From Space
World-first platform combining NASA satellite data and AI to predict shark foraging.
L'Oreal Hackathon
Active hackathon project.
Achievements
Dalyell Scholar
University of Sydney
2nd Place β Comm-STEM Γ Canva Hackathon
Sydney Urban Cooling
NCSS 2024
National Computer Science Summer School
Stellar Property Prediction (GAIA DR2)
Four-month machine learning research project analysing regression models on the GAIA DR2 stellar dataset to predict stellar parameters such as brightness, mass, and metallicity.
After performing a literature review, a research gap was identified around ML-based stellar brightness prediction. Multiple regression models were implemented and statistically evaluated using F-tests and Pearson correlation (Ξ± = 0.05).
Best models achieved ~99.6% accuracy. Polynomial regression and random forest performed strongest, while SVR showed higher error.
Alexa Horizon β Space Voice Assistant
Voice-first space-themed assistant integrating live APIs, NLP, and serverless backend infrastructure.
Built using Python, Flask-Ask, AWS Lambda, DynamoDB, REST APIs, and SSML. Tested locally on a Linux homelab using Docker and Raspberry Pi.
Currently extending into an ML pipeline for predicting movements in space-sector equities using NLP sentiment analysis.
MatchPoint β Biomechanics Tennis Platform
Computer vision-powered tennis platform built at Hack48 that analyses biomechanics and swing footage to generate personalised movement insights.
Combines dominant side detection, flexibility profiling, injury history, and CV-based swing analysis to adapt technique to an individualβs body.
Reached 200+ users within 24 hours.
Sydney Urban Cooling
Interactive web simulation showing how increasing tree canopy coverage can reduce Sydneyβs urban heat islands.
Users can draw trees, simulate temperature change, upload community readings, and view live heatmaps.
Built with HTML, CSS, JavaScript, Leaflet.js, and OpenWeather API.
Sharks From Space
World-first platform integrating real-time NASA satellite data (SWOT, MODIS, PACE) with AI to predict shark foraging behaviour with 87% validated accuracy.
Includes a novel 4D spatiotemporal Shark Foraging Index and a revolutionary non-invasive gastric biosensor for validation.
Aims to improve marine conservation and reduce shark-human conflict globally.