VirtualDetector is a research site covering High-Energy Physics, Intelligent Systems Design, and Machine Learning Systems & Optimisation. Research includes particle-physics simulation and reconstruction, cyber-physical systems, and efficient machine-learning methods, with emphasis on modelling, data processing, design exploration, and deployment.
The site provides research projects and associated publications, software, datasets, talks, posters, theses, and student projects. Topic labels link related work across research areas.
Reduced-complexity detector simulation, synthetic data generation, machine-learning-assisted particle tracking, and hardware-aware reconstruction for high-energy physics.
Design and analysis of cyber-physical and software-intensive systems, including data validation, time-series analysis, explainability, anomaly detection, and domain-specific tooling.
Efficient machine-learning model design, training, inference, and deployment across distributed, embedded, FPGA, cloud, and other resource-constrained computing platforms.
The Research Artefacts page provides a chronological record of publications, datasets, software, talks, posters, and theses, with topic labels connecting outputs across research areas.
The Student Projects page lists current and completed research projects across HEP, intelligent systems, simulation, machine learning, and computational-system design.
In case of research or student-project enquiries, you can reach us at: aW5mb0B2aXJ0dWFsZGV0ZWN0b3IuY29t