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Developed a fast, scalable and spectrally accurate numerical technique for solving nonlinear variable coefficients boundary value problems, and applying them to atmospheric wave dynamics. Beyond this I also worked with applying gradient-free optimisation techniques to solve applied mathematics problems traditionally solved using brute force techniques. Over the course of my PhD I also consulted on the application of mathematical techniques to Monash Laboratory for Optics & Applied Mathematics and the Laboratory for Turbulence Research and Combustion.

Developing a unique compressed sensing based closure for the simulation of turbulence in Large Eddy Simulations and modelled structural and flight characteristics of a waterbombing aicraft. Using Geometric Programming and a Genetic Algorithm to optimise the equations led to a 50% reduced mass compared to conventional techniques, with a calculation time of 5 seconds.

Thesis: Contour Dynamics within an Annular domain. Graduated at top of class with H1 (First Class) honours. Also attended the Australian Mathematical Sciences Institute Honours school through The University of Adelaide, receiving a HD average.

Selected as a 2023 DAAD Ai-Net Fellow for 2023.

My work builds upon a core idea: how mathematical tools can be used to glean better insights into complex systems. This includes coming up with novel approaches for considering both adversarial risk and defences in deployed machine learning systems, and work that sits at the intersection between game theory and complex nonlinear differential equations.

As part of this I am currently the project lead on a $450,000+ grant covering Certified Defences against models other than classifiers; and the research lead on two additional projects, one covering adversarial behaviours against classifiers, and the other on game-theoretic solutions to complex organisational problems.

I also spent 2 years as the research lead on a large, inter-disciplinary applied project covering the modeling, simulation, and prediction of Wireless spectrum. Over the course of this project I regularly worked with C-suite executives to provide technical expertise (including dozens of technical reports) on both modeling and managing very large data sets.

Outside of my research work, I regularly provide technical consultations to other researchers on both deep learning and how to leverage large-scale computing resources to improve research outcomes.

Teaching: Regular guest lecturing roles on graph neural networks, security, and distributed systems, and part of the team designing and lecturing a new Security Analytics Masters subject at the University of Melbourne.

Supervised PhD students: Marc Katzef (Graduated, 2023), Ifrah Saeed, Shijie Liu, Takuma Adams, Tiancheng Jiang.

Collaborators: Benjamin Rubinstein, Tansu Alpcan, Sarah Erfani, Christopher Leckie, Ella Alipourchavary (UoM), Paul Montague, Alex Kalloniatis (DST) and Justin Kopacz (Northrop Grumman).

Other activities: Member of the Security Group within the University of Melbourne School of Computing & Information Systems (CIS); TRAMx Research Entrepreneurship Participant (Best Pitch, 2023); TRAM Tracks: Project leader on developing Secure Database Services; Regular consultant on GPU-Enabled HPC within CIS; external expert on hiring AI/ML talent for DST Group Australia.

Tutoring a range of Engineering and Mathematics subjects, as well as serving as a live, text-based assistant lecturer for online lecturing, and specialist one-on-one tutoring to disadvantaged students.

As head tutor, I was also responsible for preparing materials to support a large team of other tutors.

Regularly received student appraisals above 90% positive.

Providing insights and tooling for a range of clients, including large ASX listed companies and sporting leagues. Projects included tracing millions of lost chargeback revenue; designing a dynamic bid-ranking algorithm for a tech company with hundreds of thousands of active users; a court allocation and scheduling program for a netball league; and providing advice for the Victorian Emergency Services games regarding algorithmic fairness.

Developing models that captured significant value for betting on the Australian Football League, while also pursuing arbitrarge opportunities across a range of sports. Project ended after being banned by all major Australian bookmakers at the time.