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 / 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 effectively represent and process dynamic stimuli, while using only spikes to transmit information between its billions of neurons. The Neural Engineering Framework (NEF) is a perfect fit for exploring the range of ways this can be accomplished using biologically plausible spiking neurons and synapses. I am also interested in extending the NEF to account for more detailed models of neurons and synapses, in order to deploy the NEF on various neuromorphic architectures, and to better understand the class of computations made available in neural substrate. Together, this understanding is being used to build large-scale models that process dynamic stimuli online into low-frequency and low-dimensional structured representations, on-the-fly, for use in classification/association/recall/prediction.

- A Population-Level Approach to Temperature Robustness in Neuromorphic Systems
- A neural model of hierarchical reinforcement learning
- Efficiently sampling vectors and coordinates from the n-sphere and n-ball
- A Spiking Neural Bayesian Model of Life Span Inference
- Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework
- Methods for applying the Neural Engineering Framework to neuromorphic hardware
- Extending the Neural Engineering Framework for Nonideal Silicon Synapses
- A Spiking Independent Accumulator Model for Winner-Take-All Computation
- Analysis of oscillatory weight changes from online learning with filtered spiking feedback
- 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