I build production-grade AI systems at the intersection of Physics and Computer Science — probabilistic modeling, GPU acceleration, and edge AI.
Dept. of Physics, King's College London · London, UK
Applied physics-informed neural networks to carbon nanocluster modeling, then built the GPU pipeline around repeatable experiment runs.
Kannan Industrials · London / Chennai
Architected and shipped iOS applications including 1minute DOEShelp, iPong, and DabCounter, with CoreML inference tuned for Apple Watch constraints.
Kennedy Institute of Rheumatology, Oxford · Oxford, UK
Developed CNN workflows for biomedical imaging and experimented with mixed-precision GPU training for research-grade modeling.
NHS Digital · Leeds, UK
Built an LSTM + Word2Vec NLP system for clinical note classification and anomaly detection over operational healthcare data.
Parallelised LAMMPS simulations for material discovery using AIRSS, LMP KOKKOS, OpenMP, and CUDA.
Deep RL agent achieving a 95% win rate against heuristic baselines.
CoreML pong agent optimized for Apple Watch GPU and thermal constraints.
Snapchat filter system that reached 2.88M+ views and 150K downloads.
Clinical text ICD-9 prediction system with AWS deployment experiments and anomaly detection.
Gas-optimized Ethereum smart contract that cut transaction costs by 35%.
BSc Physics
Alessandro de Vita Computational Physics Prize 2024–25. Computational physics, quantum mechanics, statistical mechanics.
MEng Computer Science
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.
Open to AI engineering, high-performance computing, and research roles where correctness, speed, and clarity matter.