Bartłomiej Chybowski

I am a PhD candidate in Biomedical Signal Processing, who is the happiest when analysing large brainwave datasets. My research interests lie in the intersection of machine learning and neuroscience. My thesis titled “Computational models of circadian and ultradian effects in seizures rooted in physiological knowledge” uses machine learning techniques and EEG signals to predict seizures and examine if they depend on circadian and ultradian patterns.

Before starting my PhD, I did an internship in a Computational Neuroscience research group supervised by Dr Klimeš at The Czech Academy of Sciences, where I used my industrial experience in data analysis to process the EEG signals. My research project titled “Improvement of multi-feature SVM model for epileptic foci localisation” used machine learning techniques to augment the localisation of epileptogenic zones in pharmacoresistant patients.

I aim to continue to develop new approaches to studying brainwave signals using state-of-the-art data science approaches to help to improve medical treatment, lower risk, reduce hospitalisation time, and improve patients’ quality of life.

Background

• PhD in Biomedical Signal Processing, University of Edinburgh, 2022 - 2025
• Internship in Computational Neuroscience, Czech Academy of Sciences, 2021 - 2022
• MSc in Computer Science, Robert Gordon University, 2017 - 2019
• BEng in Computer Science, Silesian University of Technology, 2011 - 2015