I'm a software developer/researcher working for Dr. Alan C. Evans' McGill Centre for Integrative Neuroscience at the Montreal Neurological Institute. I currently build tools to support and perform neuroscience research with supervised learning. I'm starting a PhD in Quantitative Life Sciences in September, and I'd like to explore ways to deploy neuroimaging tools beyond the lab, and use AI to do SCIENCE!
I'm interested in using deep learning and other probabilistic methods to study learning and biological intelligence, and maybe explore how emotions affect learning. I really like visualizations, and some of my brainart can be found in the in the brains section.
Parametric models of EEG power spectra can be used to design digital filters that select different components of the neural signal. Hypotheses based on these models can be validated using time-domain deep learning decoding experiments.
My master's project with Tal Arbel's Probabilistic Vision Group. We cluster features of lesions in MRI to predict future disease activity, and whether patients are likely to be responders to one of the drugs from the clinical trial.
Obtaining accurate white/gray matter segmentations is difficult because the brain's myelin is still growing. Accurate longitudinal segmentations can be used to characterize brain development.
This project uses CNNs to automatically predict whether scans will pass quality control and be useful for follow-up study.
In CBRAIN I worked mainly on implementing the RESTful API and describing it in the OpenAPI specification, as well as created the user registration system.
© Andrew Doyle 2018