Machine Learning assisted detection and prediction of climate change related anomalous events in complex marine systems

This project is a PhD Cullen MI award. It is a multidisciplinary project that aims to bridge the gap between marine sciences and machine learning in detecting climate change related anomalous events.  Anomalies are data points or patterns in data that are unusual and do not conform to a notion of normal behaviour. Anomaly detection is the task of finding those patterns in large volumes of data. It is a challenging topic because anomalous events can be abrupt, especially when they are related to climate change, to which marine ecosystems can have a complex response. Automatically detecting and correctly classifying anomalies is also difficult because data is multidimensional.  The overall goal of the project is to provide a deep learning pipeline for online short-term prediction of anomalous and/or extreme marine events which may impact the commercial aquaculture sector.

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Project Team

Assistant Lecturer and Researcher
Research Fellow