Waterloo math graduate focused on machine learning, intelligent systems, and computational neuroscience. I’ve worked across AI engineering, robotics research, and biosignal systems, and I’m especially interested in building adaptive, real-world intelligent systems.
Two sides of the same coin: using AI to study the brain, and using the brain to rethink AI
read moreBachelor of Mathematics
Computational Mathematics · Minors in CS & Cognitive Science
Across five years of co-op, research, and interdisciplinary coursework, I became increasingly drawn toward how intelligent systems learn, represent information, and adapt — both in machines and in the brain.
Where I've worked, what I've built.
VP of Workshops
Established UW Tinkerer Studio, a mentored open-source AI program.
Built out a new collaboration between DSC and HumanFeedback where students form teams to work on real open-source AI projects with mentor guidance.
ML Engineer Intern
Building evaluation infrastructure for agentic AI systems in chip design.
Designed an end-to-end evaluation pipeline for LLMs and agentic systems using LangSmith and custom tooling. Focused on token efficiency, RAG quality, tool-use reliability, and multi-step agent behavior.
Attended NASA Goddard as a Space Apps 2024 finalist.
Workshop Lead & Mentor
Ran workshops and mentored students in ML and computational neuroscience.
Led technical workshops including Intro to ML and Intro to Computational Neuroscience. Mentored students across skill levels through their first hands-on projects.
Led the local chapter of NASA's annual global hackathon.
Co-hosted AI Tinkerers meetup at Shopify Toronto.
AI/ML Developer Intern
Shipped Hailey, a women's health conversational AI, from concept to deployment.
Led development of Hailey end-to-end. Engineered prompt strategies that improved empathy and accuracy by 15-25%, built a QA logging GUI, and explored LangChain and RAG approaches with Mila Institute advisors.
ML Engineer
Biomedical ML project: reconstructing EMG signals into speech.
Worked on the Simphonic's project, building deep learning models (LSTMs and autoencoders) to reconstruct muscle signals into speech output. Contributed to the ML engineering side of cross-modal signal translation for real-time biomedical applications. The project was 1st place winner of CUCAI 2024!
AI Software Developer Intern
Deployed an invoice classification pipeline used by 500+ enterprise users.
Built a deep learning model for invoice element classification combining NLP and Neural OCR. Deployed model and backend APIs to Azure within .NET infrastructure, and designed a scalable database schema.
Became a student ambassador for Women in AI & Robotics.
Software Engineer Intern
Built real-time biosignal processing for wearable sensors.
Processed ECG, temperature, pressure, and motion signals from wearable sensors. Built a BLE-enabled Python app that replaced a legacy C# interface for real-time acquisition, integrated ML classification models, and improved data efficiency by 30%.
Research at the intersection of intelligent systems, computational neuroscience, and adaptive behavior — somewhere between a robotics lab and a neuroscience department.
A DNF model of Parkinsonian freezing of gait
Dr. Jeff Orchard, Dr. Madeleine Bartlett · Computational Neuroscience Research Group
Investigated how dopamine depletion alters action-selection dynamics in the basal ganglia through a Dynamic Neural Field (DNF) model of Parkinsonian freezing of gait (FOG). By modeling competitive interactions between direct and indirect pathways, the system reproduced characteristic freezing onset behavior under dopamine loss, providing a population-level dynamical account of impaired motor switching in Parkinson’s disease.
Learning navigation preferences under uncertainty
Dr. Michael Furlong, Dr. Chris Eliasmith · Computational Neuroscience Research Group
Explores how autonomous systems can infer human-like navigation preferences under uncertainty through inverse reinforcement learning and risk-aware control. Using a skid-steer robotic platform, the project investigates how latent cost functions, risk sensitivity, and adaptive decision-making behaviors can be recovered directly from expert demonstrations.
Reproduced and extended a biophysical ODE model of dopamine synthesis, release, and reuptake in Parkinson’s disease to study how neural activity modulates dopaminergic dynamics over time. Introduced an activity-dependent Hill-function gain mechanism linking neural firing behavior to dopamine regulation, producing emergent oscillatory behaviors absent from the original model.
Investigated biologically inspired temporal memory architectures for unsupervised novelty detection in sequential data using Legendre Memory Units (LMUs). By encoding continuous-time history through structured orthogonal memory representations, the project explored how temporal context can support anomaly detection and distribution-shift sensitivity in dynamic environments.
Languages, frameworks, and tools I work with:
full skills pageHappy to chat about comp neuro, brain-inspired AI, research, or whatever else:
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