In 2015 I graduated from Imperial College London with an MSci Physics degree after which I worked at Goldman Sachs for 5 years and developed an interest in data analytics and efficient decision modelling.

In September 2020 I joined the Interactive Artificial Intellgence CDT at the University of Bristol. After the research masters year 2020-2021 I completed my PhD from 2021-2025 and recently passed my viva! My thesis is titled ‘Bridging Neuroscience and Machine Learning: EEG Classification for Parkinson’s Disease and Beyond’.

Research Interests

My research focuses on applying machine learning to complex time series data to obtain insights through classification and forecasting. I am particularly drawn to the challenges inherent in real-world data, such as missingness, noise, and high dimensionality.

I have extensive experience with Electroencephalography (EEG) data. In one project, we developed a classification method involving a novel transformation of the EEG data, achieving high performance on early-stage Parkinson’s classification. This work was presented at the Health Intelligence workshop at AAAI 2024 link. We extended this research for the Machine Learning and Signal Processing (MLSP) 2024 conference, where we investigated combining connectivity metrics with regional statistics for classification (see projects and publications for further details).

More recently, we explored state-of-the-art deep learning methods for a range of EEG tasks, including emotion detection, mental workload, and eyes-open/closed classification. Our knowledge-based pipeline outperformed established deep learning architectures adapted for EEG, with the added advantage of interpretability via feature attribution. A pre-print of this paper is available here . Below is a figure from this work showing the increased alpha band coherence observed on average for participants with their eyes closed versus open:

My work also extends to other domains, such as applying data analysis and visualization techniques to atmospheric data. I have developed projects in Python and Unity to plot and visualize global climate trends over time. The animation below is from a Unity project featuring interactive 3D visualization.

Please see projects and publications for further details on my work!