About Me

I have recently (July 2025) joined Amazon as a Member of Technical Staff with the AGI Autonomy group in San Francisco, working on LLM post-training and RL for agents. I obtained my PhD in Computer Science from the University of Toronto and spent 2.5 years at the Vector Institute during this time. My research focused on model-based reinforcement learning and fast algorithms for discrete optimal transport. I was advised by Professors Allan Jepson and Amir-massoud Farahmand. I am broadly interested in AI, robotics and numerical optimization both as a researcher and engineer.

Prior to my PhD, I was a Staff ML Research Engineer until September 2020 at the Samsung Toronto AI Centre led by Prof. Sven Dickinson, who co-supervised the first half of my PhD. At Samsung, I spent 2.5 years working on representation learning for computer vision and vision-language integration. Before that, I spent 1.5 years at startups in Canada as a software developer and machine learning engineer.

In 2017, I completed my Master of Science in Applied Computing at the University of Toronto with a focus on natural language processing and machine learning. I received my Bachelor’s degree in electrical engineering with a minor in software engineering from McGill University.

My research philosophy emphasizes truth and precision over hype. I aim to approach scientific inquiry by continuously questioning assumptions, maintaining a balance between theory and empiricism, and remaining rigorously skeptical about everything I read and write. To quote Richard Feynman from his famous lectures on physics:

“If it disagrees with experiment, it’s wrong. And that simple statement is the key to science. It doesn’t make any difference how beautiful your guess [theory] is, it doesn’t matter how smart you are who made the guess [derived the theory], or what his name is… If it disagrees with experiment, it’s wrong. That’s all there is to it.”