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Computer Science (PhD)

University of Waterloo

Research in computational neuroscience (specifically, spatial cognition and reinforcement learning) under the supervision of Chris Eliasmith and Jeff Orchard.

• Neuro-symbolic methods for spatial representation, updating, and mapping
• Biologically plausible reinforcement learning (RL) and action selection

Computational Mathematics (Masters of Mathematics, Co-op Program)

University of Waterloo

Continuous optimization and ML

• Robust optimization of an asset pricing model applied to carbon emissions
• Courses in optimization, computational statistics, numerical analysis, PDEs, and computational neuroscience

Honours Mathematics and Physics (Bachelors of Science)

McMaster University

Mathematical physics

Neuromorphic Scientist & Engineer

Applied Brain Research

Part time research position

Research Associate

Cayuga Research

Research position at a consulting firm

Research Assistant

Ayers Research Group, McMaster University

Undergraduate summer research position

Biologically-plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-encoded Distributions

International Conference on Artificial Neural Networks

Biologically-plausible Monte Carlo Markov Chain sampling in the space of Vector Symbolic Algebra (VSA) encodings

A Recurrent Dynamic Model for Efficient Bayesian Optimization

2024 Neuro Inspired Computational Elements Conference (NICE)

Efficent Bayesian optimization implemented via recurrent neural dynamics

Improving reinforcement learning with biologically motivated continuous state representations

International Conference on Cognitive Modeling (ICCM)

Reinforcement Learning (RL) using grid cell-inspired state embeddings

Biologically-based computation: How neural details and dynamics are suited for implementing a variety of algorithms

Brain Sciences

Expanding the Neural Engineering Framework (NEF) to include complex spatiotemporal tuning curves, and then apply this approach to produce functional computational models of grid cells, time cells, path integration, sparse representations, probabilistic representations, and symbolic representations in the brain

Exploiting Semantic Information in a Spiking Neural SLAM System

Frontiers in Neuroscience

Spiking neural model of simultaneous localization and mapping (SLAM) using Spatial Semantic Pointers, a vector representation that supports symbolic operations and probabilistic encoding in neurons

A model of path integration that connects neural and symbolic representation

CogSci

A spiking path integration model using Spatial Semantic Pointers, a vector representation that supports symbolic operations

Biologically-Plausible Memory for Continuous-Time Reinforcement Learning

International Conference on Cognitive Modelling (ICCM)

Using Legendre Delay Networks as biologically-plausible memory circuits for TD learning

Optimal pricing of climate risk

Computational Economics

Improving the efficiency carbon pricing models

Accurate representation for spatial cognition using grid cells

CogSci

Model grid cells with Spatial Semantic Pointers

Simulating and predicting dynamical systems with spatial semantic pointers

Neural Computation

Use Spatial Semantic Pointers to model dynamical systems involving multiple objects and predict trajectories

ENNS Best Paper Award

European Neural Network Society

Barbara Hayes-Roth Award for Women in Math and Computer Science

Go-Bell Scholarship

Keith & Debbie Geddes Graduate Scholarship

The Emanuel Williams Scholarship in Physics