Projects
A selection of engineering tools, simulation platforms, and research projects built across automotive, aerospace, and energy domains.
Featured
Co-architected a 1M+ line compiled MATLAB desktop application for multi-domain powertrain physics simulation (mechanical, thermal, electrical), deployed as a standalone executable to 10 enterprise clients. Responsible for overall system design, module architecture, and code quality across a team of 6. Formalised plugin architecture across the organically grown codebase — introducing clear module boundaries, dependency contracts, and a plugin registry — significantly reducing coupling and onboarding time.
Designed and delivered a reduced-order vertical dynamics simulation tool combining a Simulink 18-DOF vehicle model backend with a MATLAB UI frontend. Adopted by 20 engineers across the vehicle dynamics simulation department. FRM correlated proving ground test data against high-fidelity Simpack models, reducing full simulation sweep time from 90 minutes to 90 seconds — a 60× speed-up — while maintaining accuracy within 90% of the original, enabling rapid exploratory studies previously impractical with full-complexity models.
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LHAS — Limited Handling & Stability
JLR · 2023 – 2025
Production analysis tool used daily by 20 vehicle dynamics engineers to process proving ground test data into actionable handling and stability insight.
ATOM — Application Toolbox for Objective Metrics
JLR · 2023 – 2025
Vehicle build tracking platform with a SQL database backend and MATLAB UI frontend, used by 500 engineers across JLR as version control for vehicle configurations throughout the development programme.
ePOP Concept
ZeBeyond · 2025 – Present
Backend API for a 500k line web-based simulation platform. Designed and built a REST API integrating AWS DynamoDB with an Angular front-end, enabling real-time architecture composition from a live component database. Includes combinatorial search across up to 500,000 architecture permutations.
Ride Comfort Prediction — PhD Research
HORIBA MIRA / Coventry University · 2015 – 2019
Built signal processing pipelines and trained ANNs to predict human ride comfort from biometric and vibration data. Published in Neural Computing and Applications, Springer (2019).
ADAS Sensor Degradation Study
Coventry University (IFTC) · 2019 – 2020
Investigated image sensor degradation under real-world noise conditions for ADAS applications. Trained DNN classifiers using MATLAB Automated Driving Toolbox to assess L2+ robustness.
Fleet Data Pipeline
JLR · 2020 – 2023
Designed the data pipeline architecture for a 350-vehicle engineering fleet into GCP / BigQuery, later expanded by the Data Science team into organisation-wide chassis and ADAS analytics.