Astrophysics PhD Candidate, Monash University
2022 - 2025
My research is mainly focused on neutron stars, specifically p ulsar timing. My main project is on improving pulsar timing by characterising pulse shape variation, which I have continued from my honours research. This method can characterise pulsar shape changes as well as improve timing. I hope to use this method to improve our sensitivity to the gravitational wave background. I am simultaneously assisting with the noise modelling for the third data release for the Parkes Pulsar Timing Array.  I am also working on a multimessenger gravitational wave project looking at ​coincident detection ​statistics for compact mergers with electromagnetic counterparts. Alongside my research, I am ​involved in many outreach activities and mentoring.

Honours in Astrophysics, Monash University 
2021 
2021 First-Class Honours
2021 Recipient of the J.L Williams Scholarship
2021 Faculty of Science Dean’s List Award
2022 Rhodes Scholarship Shortlist 

The honours degree consists of two components, coursework and a research project. For my coursework I have undertaken units in computational astrophysics, stellar astrophysics, advanced observational astronomy, general relativity, advanced data analysis and machine learning. My research was validating a new dynamic pulsar timing method on simulated pulsar data. I graduated with first-class honours.

Bachelor of Science and Bachelor of Commerce, Monash University 
2017 — 2020 
Majors: Astrophysics and Business Analytics, Minor: Mathematics 
Weighted Average Mark: High Distinction 

As part of my degree, I worked on cutting-edge research projects with professional astrophysicists, analysing telescope data with innovative techniques including neural networks, and other machine learning techniques. My research is outlined in more detail below. During my studies I also completed courses in machine learning, time series analysis, forecasting, Bayesian statistics, data visualisation, databases and computing. As part of a group project for Business Analytics my group created a machine learning model that was the best at predicting the outcome of a point in tennis. Our model correctly predicted 92.86% of point outcomes, which was higher than the other 28 models submitted. 

Year 12 Graduate, Shelford Girls' Grammar  
2016 
ATAR: 96.65