Nolan Dey

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.

Research
Identifying and interpreting tuning dimensions in deep networks
Shared Visual Representations in Human & Machine Intelligence NeurIPS Workshop, 2020
Nolan S. Dey, J. Eric Taylor, Bryan P. Tripp, Alexander Wong, Graham W. Taylor
37,000 Human-Planned Robotic Grasps With Six Degrees of Freedom
IEEE Robotics and Automation Letters, 2020
Victor R. Osorio, Rajan Iyengar, Xueyang Yao, Preshish Bhattachan, Adrian Ragobar, Nolan S. Dey, Bryan Tripp
Projects
Actor-Critic RL using Spiking Neurons
Nolan S. Dey

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

Synthesizing Preferred Inputs for Deep Neurons via GANs
Nolan S. Dey*, Nick Torenvliet*, Austin Kothig*

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.

Graph Convolutional Neural Network Explainability
Nolan S. Dey

Released the only open-source implementation of “Explainability Methods for Graph Convolutional Neural Networks” by Pope & Kolouri et al. using PyTorch-Geometric and RDKit

SYDE 2019 Class Survey
Nolan S. Dey, Jason Manson-Hing

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