Hi, I'm Nathan!
I'm a Computer Science (Honours) Student @ The University of Adelaide and Software Engineer.
I'm a Computer Science (Honours) Student @ The University of Adelaide and Software Engineer.
A web application for tracking hypertrophy and strength training, allowing users to create and manage personalised workout plans. Features include a drag-and-drop workout planner, exercise logging, and adaptive progression recommendations. The app leverages React for interactive user experiences and Next.js for server-side rendering, ensuring fast and efficient performance. Built with Tailwind CSS for a responsive design, it provides a seamless experience across devices.
An NBA Fantasy Draft Projection tool designed to help users predict player stats for the 2023-24 season. Leveraging historical player data from Kaggle, the model generates accurate projections through normalisation, similarity analysis, ranking, and weighted averaging. The dashboard improves fantasy draft strategies by providing a competitive edge with detailed player projections. The UI includes a filterable Top 100 players table, dynamic bar and radar graphs, and customisable sliders, allowing users to tailor projections to their fantasy league's scoring system. Pre-set scoring systems for platforms like ESPN are also available for convenience.
A web platform designed to assist student clubs with promotion, recruitment, and member engagement. I led a team of four to develop a user-friendly interface, prioritising accessibility for a diverse student audience. The backend, built with Express.js, Node.js, and MySQL, ensures seamless and efficient functionality. Key features include event promotion tools, member management, and club registration, providing an all-in-one solution for club administrators and students seeking to connect with campus organisations. The project earned me a perfect assessment score, reflecting my significant contributions to both frontend and backend development.
Python
C++
C
TypeScript
JavaScript
Julia
Java
R
MATLAB
React
Angular
Tailwind CSS
HTML
CSS
MySQL
Prisma
Next.js
Express
Flask
Docker
TensorFlow
PyTorch
scikit-learn
Keras
Nov 2024 – Present
Nov 2023 – Feb 2024
During my internship, I worked on developing optimal camera placement algorithms for the Art Gallery Problem using Julia, Python, and C++. A key focus of the project was benchmarking Julia's computational efficiency against Python and C++, where I achieved a 17x speed improvement over Python and performance comparable to C++.
This work demonstrated Julia’s capability to handle data-intensive tasks effectively, combining the ease of use of Python with the computational power of C++. By showcasing Julia's potential to streamline development processes, I highlighted its value in reducing the need for language translation workflows, saving significant development time for future Modelling and Analysis projects.
Expected Dec 2025
Nov 2024
GPA 6.54 / 7.0
Activities and societies
Awards
