Software Engineer & Researcher
I build production-grade AI systems at the intersection of Physics and Computer Science, spanning probabilistic modeling, GPU acceleration, and edge AI.
Shipped clinical, research, and consumer ML systems.
CUDA, PyTorch, TensorFlow, and performance profiling.
PINNs, uncertainty, and computational physics.
Optimized inference on mobile and watch hardware.
Readable, monitored, repeatable systems.
Each role is framed around the operating environment, the technical system, and the result. The images make the timeline scannable without turning it into a logo wall.
Dept. of Physics, King's College London
Applied physics-informed neural networks to carbon nanocluster modeling, then built the GPU pipeline around repeatable experiment runs.
Kannan Industrials
Architected and shipped iOS applications including 1minute DOEShelp, iPong, and DabCounter, with CoreML inference tuned for Apple Watch constraints.
Kennedy Institute of Rheumatology
Developed CNN workflows for biomedical imaging and experimented with mixed-precision GPU training for research-grade modeling.
NHS Digital
Built an LSTM + Word2Vec NLP system for clinical note classification and anomaly detection over operational healthcare data.
A portfolio section should prove engineering range quickly: simulation, reinforcement learning, mobile inference, AR, NLP, and smart contracts.
The education section now carries the same visual weight as work history, with image-backed institute cards and concise academic context.
BSc Physics
Alessandro de Vita Computational Physics Prize 2024-25. Modules include computational physics, quantum mechanics, and statistical mechanics.
MEng Computer Science
Coursework across algorithms, machine learning, theory of computation, and distributed systems.
A Levels
A*A*A* in Mathematics, Further Mathematics, and Physics. 7 A*s and 4 As at GCSE.
Grouped for how engineering conversations actually happen: language, modeling, cloud systems, and research tooling.