PhD Candidate

I started my masters in Computer Science at the lab in September 2013, and fast-tracked towards a PhD in September 2014. I received my BMath Double Honours in Computer Science and Combinatorics & Optimization from the University of Waterloo, and have had software development internships involving machine learning and "big data" at companies such as Google, Amazon, and ContextLogic. I also helped start-up the multi-billion dollar company, Wish, by developing their personalization/relevancy algorithms.

I am interested in understanding how the brain can most effectively represent and process dynamic stimuli. The NEF is a perfect fit for exploring the range of ways this can be accomplished using biologically plausible spiking neurons and synapse models. This understanding is being used to build models that classify dynamic stimuli online, and to build structured representations of low-frequency information, on-the-fly, for use in tasks such as recall/association/prediction.

- A Population-Level Approach to Temperature Robustness in Neuromorphic Systems
- Efficiently sampling vectors and coordinates from the n-sphere and n-ball
- Extending the Neural Engineering Framework for Nonideal Silicon Synapses
- Improving With Practice: A Neural Model of Mathematical Development
- Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch
- Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework
- Improving with Practice: A Neural Model of Mathematical Development
- Computing with temporal representations using recurrently connected populations of spiking neurons
- A Solution to the Dynamics of the Prescribed Error Sensitivity Learning Rule
- Nengo: A Python tool for building large-scale functional brain models
- Learning large-scale heteroassociative memories in spiking neurons