Jevi Waugh

About Me

I recently completed a Bachelor of Computer Science majoring in Machine Learning and have commenced a Master of Data Science at The University of Queensland. My studies focus on advanced statistics, numerical linear algebra, and deep learning, which form the mathematical and computational foundations of modern data science and machine learning.

I am particularly interested in understanding how mathematical ideas translate into real systems. Much of my work explores how machine learning models behave internally and how theoretical insights can guide the design of better algorithms.

Learning frequency domain codes for semantic vision
Learning frequency domain codes for semantic vision

Frequency

Mar 27, 2026

2026 Research Experiences Showcase Talk

This is a recording of the academic showcase hosted by the University of Queensland highlighting research conducted by us.

Mar 27, 2026

2026 Research Experiences Showcase
2026 Research Experiences Showcase

An academic showcase hosted by the University of Queensland highlighting research conducted by students in the 2026 Summer Programs. I will showcase my recent research in that event.

Mar 27, 2026

Analysis of Gallstones
Analysis of Gallstones

Aug 1, 2025

Machine Learning-Based Classification of Table Tennis Swings Using Racket Kinematics
Machine Learning-Based Classification of Table Tennis Swings Using Racket Kinematics

This report explores using machine learning to classify age and gender from racket kinematics during table swings, using a modified dataset from DRYAD. It compares KNN, SVM, and One-vs-Rest Logistic Regression, revealing how racket motion features influence classification performance.

May 7, 2025

Leveraging Persistent Homology for Topological Feature Extraction in Machine Learning: The PH-AML Pipeline
Leveraging Persistent Homology for Topological Feature Extraction in Machine Learning: The PH-AML Pipeline

PHAML enhances machine learning by integrating topological features from persistent homology with traditional data inputs, capturing global structure through Vietoris–Rips filtrations. This approach improves classification accuracy and robustness compared to baseline models using only conventional features.

Apr 1, 2025

Fine-Tuning FLAN-T5 for Biomedical Lay Summarisation (BioLaySumm 2025)
Fine-Tuning FLAN-T5 for Biomedical Lay Summarisation (BioLaySumm 2025)

FLAN-T5 fine-tuning and LoRA for biomedical lay summarisation. Full implementation on GitHub.

Jan 1, 2025