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Published in AAAI Health Intelligence Workshop (pending publication in Computation Intelligence Springer Book Series), 2023
We use Electroencephalography (EEG) data to detect early stage Parkinson’s Disease. Firstly, we present a novel representation for EEG data, a 7-variate series of band power coefficients, which enables the use of (previously inaccessible) time series classification methods. Our approach achieves over 90% accuracy, recall and precision which rivals state of the art methods. This is particularly impressive given the early stage Parkinson’s Disease participants and limited optimisation of the model thus far. Secondly we present a framework for determining the importance of individual brain regions in Parkinson’s Disease classification. We find that across different EEG data types, it is the Prefrontal brain region that has the most predictive power for the presence of Parkinson’s Disease
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Postgraduate course, University of Bristol, Computer Science Department, 2022
Teaching Assistant for MSc course covering Machine Learning methods across supervised learning (classification and regression), probabilistic methods (Bayesian networks and Markov decision processes) as well as genetic algorithms and multi-agent systems. Key responsibilities included running problem sheet classes and assessing final projects. Please find further information on the course here
Undergraduate course, University of Bristol, Computer Science Department, 2022
Teaching Assistant for final year undergraduate course covering Machine Learning methods across supervised learning (classification and regression), probabilistic methods and ensemble methods. Key responsibilities included running problem sheet classes and assessing final projects. Please find further information on the course here
Postgraduate course, University of Bristol, Computer Science Department, 2023
Teaching assistant for postgraduate Advanced Data Analytics course. This course spans machine learning methods, data visualisation techniques and Natural Language Processing (NLP) methods. Machine learning methods covered range from dimensionality reduction and clustering to probablistic models and deep-learning neural networks. Data visualisation is primarily done via Python and Tableau. NLP methods covered include Long Short Term Memory networks, Recurrent Neural Networks and Transformers. Please find further information on the course here