About Me
I am an incoming Senior Machine Learning Scientist at Visa, where I will be working on fraud detection and prevention using machine learning. Before that, I was a Research Fellow at the University of Southampton, where I applied machine learning techniques to maritime engineering challenges, collaborating with industry partners to optimise car carrier designs through estimating wave-induced resistance.
My Ph.D. research investigated how autonomous agents develop languages from scratch, with a focus on temporal dynamics in those languages. My work examining these temporal relationships in interpretable emergent communication has been published in leading conferences including NeurIPS and ICLR. Since 2025, I have also been a reviewer for NeurIPS, where I try to avoid becoming Reviewer 2.
- Artificial Intelligence
- Computational Linguistics
- Emergent Communication
- Reinforcement Learning
- Data-Centric Engineering
PhD in Artificial Intelligence, 2020–2025
University of Southampton
BSc in Computer Science, 2016–2020
University of Liverpool
Skills
PhD in Artificial Intelligence.
All publications and projects coded in Python.
Most of my work is in PyTorch (Lightning ⚡️).
Worked with SLURM, Linux and HPC both during PhD and at Intel.
Automation pipeline, CI/CD testing.
Getting to know the container orchestration platform.
Homelab Kubernetes cluster with Nextcloud, GitLab, IoT, email and others!
Made prototype-level PCBs.
Recently experimenting with sourdough bakes (focaccia 🍞!).
My new favourite form of transport.
Experience
- Joining the fraud detection and prevention team.
- Expertise: TBC
- Led AI implementation for a 2-month industry partnership optimising car carrier hull designs.
- Architected custom neural networks that accurately predict wave-induced resistance.
- Developed specialized loss functions tailored for maritime experimental data analysis.
- Delivered predictive models that accelerated added resistance calculations by 100x compared to traditional semi-empirical methods.
- Expertise: Scientific ML, PyTorch, MLOps (MLflow), Python
- Thesis: Temporal Dynamics in Emergent Communication
- Supervised by Prof. Timothy Norman, Prof. Adam Sobey, and Prof. Federico Cerutti.
- Supported by UKRI EPSRC funding through the MINDS CDT
- Investigated the effect of time on communication protocols, improving agents’ strategy development and convergence speed by up to 50%.
- Designed an architecture necessary for the emergence of temporal referencing in agent communication, enabling efficient communication.
- Developed human-interpretability measures for an emergent language containing local spatio-temporal relationships.
- Built end-to-end ML pipelines for agent training and evaluation.
- PhD defended with no corrections.
- Expertise: AI/ML, Python, PyTorch (Lightning), Ray (RLLib).
- Professional Skills: Research impact, research communication, academic collaboration.
- Managed client-facing HPC and Cloud facilities, ensuring optimal performance.
- Collaborated on pre-production hardware projects, developing technical solutions.
- Created deployment documentation and automation scripts to improve system efficiency.
- Expertise: HPC, Cloud, Bash, Linux, Server Hardware.
- Developed Bash and SQL scripts for customer data analysis using AWS services.
- Progressed from intern to part-time role across multiple projects.
- Expertise: Big Data, Amazon AWS (EC2, S3, Redshift), SQL, Bash.
Publications
Talks & Events
