My PhD research is focused on understanding the different states and levels of consciousness (the neural substrates or mechanisms that correspond to our, irrespective of content, general ability to experience), by identifying novel electrophysiological signatures of the brain under general anesthesia (GA). Specifically, I'm working on the assessment of various levels of unconsciousness by using electroencephalographic (EEG) measures of brain activity and deep learning methodologies. These predictive models open the possibility of a data-driven approach for acquiring neuroscientific knowledge, but also allow the creation of automated end-to-end systems for clinical use (e.g. for monitoring the depth of anesthesia, or for diagnosis and prognosis of patients with disorders of consciousness), a domain where deep learning has shown prominence.
I am a member of the following research groups:
My research interests and activities span the following areas:
CO320: Introduction to Object-Oriented Programming
CO520: Further Object-Oriented Programming
CO551: Data Structures and Algorithms
CO553: Agile Development and Software Secutiry B
CO554: Computing Theory and Concurrent Programming
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