About
I am/was an M.Sc. graduate student at the Probabilistic Vision Group (PVG) under the supervision of Prof. Tal Arbel at McGill University and Mila Quebec AI Institute. I worked on researching applications of deep learning in the medical domain, specifically, related to 3D brain imaging (MRI) data and neurological diseases such as Multiple Sclerosis (e.g. disease prediction, segmentation, and detection) and Alzheimer's disease. My focus was on applications of computer vision, multimodal learning (how to combine clinical data with imaging data), meta-learning, attention mechanisms, and transformer models. I also enjoy product development and tinkering with all sorts of tools along the way (full stack, software engineering, mobile dev, hardware, etc.). Also a fan of outdoor sports, airsoft, and video games :)
You can reach me by messaging me on LinkedIn!
Research Interests
- Applied Deep Learning
- Meta-learning
- Multimodal machine learning
- Transformers and Attention Mechanisms
Education
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M.Sc. (Thesis), Electrical & Computer Engineering
McGill University, Montreal, Canada
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B.Eng., Computer Engineering (w/ Distinction)
Ryerson University, Toronto, Canada
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Summer Engineering & Language Program
Université Grenoble Alpes, Grenoble, France
Experience
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Research Assistant
Ryerson University, Canada
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FET Lead, Platform Architect (PEY)
AMD, Canada
Publications
Consensus learning with multi-rater labels for segmenting and detecting new lesions (competition paper)
NICHYPORUK, B., VASILEVSKI, K., HU, A., MYERS-COLET, C., CARDINELL, J., SZETO, J., FALET, J.-P., ZIMMERMANN, E., SCHROETER, J., ARNOLD, D. L., AND ARBEL, T. Consensus learning with multi-rater labels for segmenting and detecting new lesions. Multiple Sclerosis New Lesions Segmentation Challenge, Medical Image Computing and Computer Assisted Interventions (2021). Accepted
2021, made with in pure Bootstrap 4, inspired by Academic Template for Hugo