I am a MASc student in Systems Design Engineering at the University of Waterloo, where I am supervised by Bryan Tripp, Graham Taylor, and Alexander Wong. My research has focused on developing techniques to understand/explain the activation space of deep neural networks. Some of my research interests include neural network explainability, biologically-inspired deep learning, generative modelling, and understanding why deep networks generalize. I also run a Deep Learning Reading Club.
I completed my BASc in Systems Design Engineering at the University of Waterloo, where I completed machine learning internships at Mind Foundry, Apple, and Capital One and software engineering interships at Parabol, Connected, and Kik.
Released the only open-source implementation of “Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons” by Frémaux et al. using Nengo and OpenAI Gym
Released an open-source PyTorch implementation (originally Caffe 1.X) of “Synthesizing the preferred inputs for neurons in neural networks via deep generator networks” by Nguyen et al.
Released the only open-source implementation of “Explainability Methods for Graph Convolutional Neural Networks” by Pope & Kolouri et al. using PyTorch-Geometric and RDKit
Surveyed 55 respondents from my undergraduate class with questions related to demographics, academics, internships, lifestyle, and post-graduation plans. Published a detailed analysis of the survey results with 109 graphs and open-sourced my code to help future classes conduct similar surveys