I started my masters in Computer Science at the lab in September 2013, and fast-tracked towards a PhD in September 2014. I successfully defended my PhD, titled "Dynamical Systems in Spiking Neuromorphic Hardware", in April 2019. 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 now work for Applied Brain Research, as a Senior Research Scientist, helping develop the next generation of spiking neuromorphic algorithms and applications.
My thesis addresses our understanding of 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. We have extended the NEF to account for more detailed models of neurons and synapses, in order to deploy the NEF on neuromorphic architectures such as Braindrop and Loihi, 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 structured representations, for use in classification, association, and prediction.