Dominic Boutet (CV)
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I am a PhD student at the Baillet Lab. I have a BSc in Neuroscience (Major) and Computer Science (Minor) from McGill University. My main interests relate to computational models of brain systems as tools for performing inference on neural processes and making predictions on healthy vs. diseased brain trajectories. I have special interests in dynamical systems, machine learning, and mental illnesses. Outside of research, I enjoy working out and playing the electric guitar. I am also the TA for NSCI 200, the introductory class in Neuroscience at McGill.
As an undergraduate research intern I worked with brain-scale dynamical network models and MEG resting state data. I focused on model optimization and parameter space exploration to investigate the ability of those models to encapsulate individual-specific spectral features and perform virtual neural fingerprinting. I also worked on a novel parameter space reduction approach to facilitate parameter space exploration in high-dimensions [preprint]. I was also featured in a friend's work [now published] on cardiovascular imaging data analysis which focused on biomarker identification. I contributed to the design and implementation of the feature selection approach for biomarker identification and of the machine learning classifier based biomarker validation pipeline that leverages non-parametric null testing to assess the significance of the biomarkers in predicting subject condition (healthy vs diseased).
I am currently investigating the effect of including individual structural connectivity data within dynamical network models to encapsulate the individual-specific features in MEG data. I am also collaborating with Dr. Lena Palaniyappan from the Douglas Research Centre on a few projects regarding sensory encoding in patients diagnosed with, or at risk for, schizophrenia. I am developing and maintaining an open source package called pystorm which acts as a python port of some functions from the Brainstorm toolbox. It aims to be lightweight, fast (through GPU support and JIT compilation when possible), and hopefully easily scalable to high performance computing [see github repo for details]. Available through pypi as pystorm3.