I’m an AI/ML and computer vision specialist passionate about addressing complex technical challenges. My work spans a range of projects, from developing a deep learning–based exercise recognition system that achieves over 99% accuracy in real-time motion analysis, to designing a reliable fiducial marker system for precise tracking and identification. I have also contributed to advanced sensor calibration and fusion techniques, and explored shape matching methods. I hold a PhD in Computer Science from McGill University, where I specialized in computational complexity theory. I combine rigorous academic research with hands-on algorithm development to create novel solutions for complex problems in AI and computer vision.

Education

PhD in Computer Science
McGill University (Canada)
The fine-grained complexity of constraint satisfaction problems

MSc in Computer Science
McGill University (Canada)
The complexity of constraint satisfaction problems and symmetric Datalog (Dean’s Honour List)

BSc in Psychology
McGill University (Canada)
First Class Honours

Continuing Professional Development

Machine Learning & Deep Learning

  • Generative AI with Large Language Models (2023)
    Course by DeepLearning.AI and Amazon Web Services on Coursera
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  • TensorFlow 2 for Deep Learning Specialization (2022)
    3-course program by Imperial College London on Coursera
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  • Machine Learning (2022)
    Course by Stanford University on Coursera
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  • Deep Reinforcement Learning 2.0 (2022)
    Course by Hadelin de Ponteves, SuperDataScience Team, Ligency Team on Udemy
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  • Deep Learning Specialization (2021)
    5-course program by DeepLearning.AI on Coursera
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  • TensorFlow: Advanced Techniques (2021)
    4-course program by DeepLearning.AI on Coursera
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Computer Vision & Autonomous Vehicles

  • First Principles of Computer Vision (2022)
    5-course program by Columbia University on Coursera
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  • Self-Driving Cars (2021)
    4-course program by the University of Toronto on Coursera
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