This project will develop advanced machine learning and stochastic optimisation methods to identify anomalous behaviours in maritime traffic using Automatic Identification System (AIS) data. It will enhance maritime situational awareness and security while addressing uncertainty and complexity in vessel behaviour modelling.
The maritime domain is critical for global trade and economic stability, yet faces threats from anomalous behaviours such as illegal fishing, smuggling, and piracy. Automatic Identification System (AIS) data offers a rich source of real-time vessel movement information. However, detecting anomalies within such high-dimensional, noisy, and incomplete datasets remains a challenge.
This PhD project aims to bridge machine learning (ML) and stochastic programming for robust maritime anomaly detection. The student will:
• Develop ML-based models (deep learning, unsupervised clustering, probabilistic models) to classify and predict vessel behaviours from AIS data.
• Formulate stochastic optimisation models to address uncertainty and incomplete data, enabling decision-support for anomaly identification.
• Explore hybrid ML–optimisation approaches to enhance detection accuracy, interpretability, and scalability.
• Validate methods on real-world AIS datasets, benchmarking against existing anomaly detection methods, and collaborate with maritime stakeholders.
The project will contribute to complex integrated systems research, providing a robust framework for maritime domain awareness, supporting sustainable shipping, and enhancing global maritime security.
Please find more information about the application here: https://www.southampton.ac.uk/research/institutes-centres/epsrc-mod-centre-for-doctoral-training-in-complex-integrated-systems-1
Please direct any questions to the supervisor Dr Hongyu Zhang, hongyu.zhang@soton.ac.uk