My research sits at the intersection of safety-critical artificial intelligence, physics-based modelling, sensing, biomechanics, and functional safety.

Two connected research streams currently define much of my work:

I investigate how vehicle safety systems can better detect, interpret, and respond to difficult or atypical safety scenarios, particularly non-upright pedestrians and vulnerable occupants.

This programme connects:

• multimodal sensing and sensor fusion;
• computer vision and uncertainty-aware AI;
• forensic biomechanics;
• pedestrian-impact reconstruction;
• injury-risk modelling;
• autonomous vehicle and ADAS safety; and
• ISO 26262-aligned functional-safety architectures.

The broader objective is to move from post-event forensic reconstruction toward proactive detection, intervention, and injury prevention.

A second major research stream examines battery State-of-Health (SoH) from both machine-learning and functional-safety perspectives.

My recent work investigates:

• battery SoH estimation;
• validation inflation and model generalisation;
• cross-cell transfer reliability;
• predictor structure;
• battery-health uncertainty;
• EV power-margin integrity; and
• the role of battery condition in safety-critical vehicle functions.

The central question is not only whether a model performs accurately on familiar data, but whether its predictions remain reliable when transferred to previously unseen cells and real operating conditions.

Research principle: High apparent accuracy is not enough for a safety-critical system. The system must remain reliable when the operating conditions, data distribution, or physical system changes.

Earlier and parallel research includes geospatial intelligence, remote sensing, satellite-data analysis, computational astrophysics, stellar-population modelling, and galactic evolution.

Predictor Structure Modulates Validation Inflation and Cell-Transfer Reliability in Battery State-of-Health Estimation

This study examines an important limitation in machine-learning-based battery SoH estimation: strong validation performance does not necessarily imply reliable generalisation to an unseen battery cell.

The work investigates how predictor structure, validation strategy, and cell-to-cell transfer influence apparent model performance and cross-cell reliability.

A central implication is that battery-health models should be judged not only by their accuracy on familiar data, but also by their ability to transfer reliably to previously unseen cells.

Battery State-of-Health as a Functional Safety Variable: an ISO 26262-Aligned AI Framework for Electric Vehicle ADAS Power Integrity

This research treats battery State-of-Health as a functional-safety variable, rather than solely as a maintenance indicator.

It presents a five-layer architecture linking:

SoH estimation → power-margin monitoring → safety decision logic → vehicle response → lifecycle management

The framework examines how battery degradation and auxiliary electrical demand may affect the power margin available to safety-critical ADAS functions.

A Physics-Grounded Multi-Modal Sensor Fusion Framework for Pedestrian Impact Kinematic Reconstruction Under Uncertainty: Phase 1 Design and Theoretical Evaluation

This work develops a physics-grounded framework combining multimodal sensing, kinematic reconstruction, uncertainty propagation, and forensic interpretation for pedestrian-impact analysis.

A Multi-Modal AI System for Detecting Pedestrians Lying on the Road: Simulation-Based Safety and Injury Risk Analysis

This research investigates one of the difficult edge cases in automated vehicle safety: detecting pedestrians who are already lying on the road.

The work combines multimodal sensing, AI-based detection, simulation, and injury-risk analysis to examine how improved perception could support earlier intervention.

• Autonomous vehicle and ADAS safety
• Detection of fallen and non-upright pedestrians
• Vulnerable-road-user protection
• Safety-critical perception
• Functional-safety decision systems
• ISO 26262-aligned safety architectures

• Multimodal artificial intelligence
• Sensor fusion
• Computer vision
• Machine learning
• Uncertainty-aware decision systems
• Model validation and generalisation
• Cross-domain and cross-system transfer

• Pedestrian-impact reconstruction
• Injury mechanisms
• Vehicle-occupant safety
• Wheelchair occupant protection
• Kinematic reconstruction
• Uncertainty quantification
• Accident reconstruction

• Battery State-of-Health estimation
• Battery-health prediction
• Validation inflation
• Predictor structure
• Cell-to-cell transfer reliability
• EV power-integrity modelling
• Battery-informed functional safety

• Geospatial intelligence
• Remote sensing
• Satellite-data analysis
• Computational astrophysics
• Stellar-population modelling
• Galactic evolution

• From Post-Mortem to Prevention: Redefining “Invisible” Pedestrians through ISO 26262 and Multi-Modal AI — SSRN, 2026
• Integrated Safety Architectures: Leveraging Multi-Modal AI and ISO 26262 to Protect Vulnerable Road Users — SSRN, 2026
• Sudden Incapacitation or Death at the Wheel: Unravelling the Predictors of Catastrophic Multi-Vehicle Collisions — SSRN, 2026

• Mechanisms, Modelling, and Machine Learning-Based Prediction of Lithium-Ion Battery Degradation in Electric Vehicles: A Comprehensive Review — SSRN, 2025

• Global Homeland Security Satellite Imagery Market: Strategic Outlook and Growth Trajectories — SSRN, 2025
• SATCOM, the Future UAV Communication Link — SSRN, 2022

• Galactic Archaeology: A Chemo-Kinematic Review of the Milky Way's Hierarchical Assembly — SSRN, 2025
• Galactic Paleontology: Reconstructing Accretion Events with Chemo-Dynamical Signatures — SSRN, 2025
• Unveiling Galactic Assembly: Chemo-Kinematic Insights from Stellar Absorptions — SSRN, 2025

I welcome research and technical collaboration in:

• safety-critical artificial intelligence;
• automotive and autonomous-system safety;
• vulnerable-road-user protection;
• multimodal sensing and sensor fusion;
• forensic biomechanics and accident reconstruction;
• ISO 26262 and functional-safety engineering;
• electric-vehicle battery intelligence;
• battery State-of-Health estimation and validation;
• geospatial intelligence and remote sensing;
• scientific computing; and
• computational astrophysics.

For research, technical, or professional enquiries, please connect with me through: