Mathematical Optimisation

Maximize efficiency and reduce operational costs through strategic optimisation. I specialise in tailoring algorithms that solve complex placement, scheduling, and resource allocation problems—underpinned by a robust background in numerical methods and HPC.

My Background

I worked at the Australian National University, as a Research Fellow, where I was primarily tasked with applying optimisation techniques to difficult problems. During this time I became widely read in a variety of optimisation techniques and developed a variety of solutions for the projects I was assigned to.

What I Offer

I specialise in mathematical optimisation, developing efficient algorithms that solve complex scheduling, resource allocation, and computational problems. With expertise in Python, CUDA, and high-performance computing, I implement cutting-edge optimisation techniques that leverage parallel processing and probabilistic sampling to find optimal solutions quickly and reliably.

Expertise in Optimisation Techniques

I employ a range of mathematical optimisation methods tailored to real-world applications, including:

• LIPO (Lipschitz Optimisation) – A powerful approach for global optimisation when the objective function is expensive to evaluate. By efficiently exploring the search space, LIPO is particularly useful for hyperparameter tuning, scientific modelling, and AI-driven decision-making.
• Thompson Sampling & Bayesian Optimisation – Used when exploration and exploitation must be balanced.
• Powell’s Method & Linear Descent Techniques – Applying gradient-free approaches to optimise functions where derivatives are unavailable or unreliable.
• Gradient-Free Optimisation Methods – Implementing evolutionary algorithms, particle swarm optimisation, and simulated annealing where gradient-based approaches are impractical.
• Classic Numerical Optimisation – Applying standard techniques to efficiently find local optima in well-behaved functions.

What I Don’t Do

• Military applications
• Not all optimisation algorithms fall within my mathematical background.

Who I Work With

• Research institutions and R&D teams
• Industrial and scientific software developers
• AI and data science teams in scientific domains
• Engineering and modelling specialists
• Agtech firms

Why Work With Me?

• Deep expertise in mathematical modelling, probabilistic inference, and high-performance computing.
• Efficient use of GPU acceleration (CUDA) to reduce computational overhead in complex evaluation functions and scale optimisation workflows.
• Custom solutions tailored for industry-specific challenges.

If you need efficient mathematical optimisation solutions that leverage state-of-the-art techniques let’s work together to build high-performance decision-making systems.