Welcome! I am an Associate Applied Machine Learning Specialist at Vector Institute. I recently completed my HBSc from the University of Toronto studying computer science, evolutionary and human biology. In my final year as an undergraduate, I was fortunate to work with Marzyeh Ghassemi and Sheldon Huang on unsupervised out of distribution detection.

My interests revolve around systems for ML, interpretability and out of distribution detection. I enjoy working on machine learning problems that are engineering heavy. Particularly the ones that involve training large models, clean abstractions and careful code optimizations. My goal is to train and understand expressive, large parameter models that can bring us closer towards an understanding of artifical and biological intelligence.

In my spare time, I enjoy staying fit, reading books and learning about the peculiarities of CPython. I’m currently reading The Skeptics’ Guide to the Universe. Please feel free reachout if you’d like to chat.

---

Recent Work

Oct 21, 2021	The code our team was reviewing for is officially released! Paper: Parameter Prediction for Unseen Deep Architectures. Code: ppuda.
Jun 9, 2021	We open sourced vector_cv_tools. A toolkit to assist with computer vision research.