I am a Research Scientist at Cerebras Systems where I research methods for more compute-efficient training of large-scale neural networks.
I received my MASc in Systems Design Engineering at the University of Waterloo, where I was supervised by Bryan Tripp, Graham Taylor, and Alexander Wong. My research focused on developing techniques to understand and explain the activation space of deep neural networks. I also ran a Deep Learning Reading Club.
I also received 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
Founded and annually organized an event where students sing Christmas carols and ask for food donations. Between 2012 and 2019 we donated ~10,314 food items to GTA food banks since 2012, with 599 students participating over 9 years!