I'm a third-year Electrical Engineering student at the University of Waterloo with a passion for building systems that solve meaningful problems — from embedded hardware to AI-driven decision-making.
Currently working as a Machine Learning Engineer Intern at Health Canada (PMRA), where I apply AI to regulatory science and public health challenges. I also lead an 11-person AI research team at Wat.AI, developing reinforcement learning agents for renewable energy optimization in Sub-Saharan Africa.
As a Cansbridge Fellow, I've received a $10,000 fellowship supporting an upcoming Asia internship (September-December 2026), fostering cross-cultural technical collaboration. I also serve as VP Services for the UWaterloo Engineering Society, where I work to improve the student experience for the entire engineering community.
From FPGA traffic control systems to natural language course scheduling, I love bridging the gap between theoretical knowledge and practical impact. Whether it's teaching C++, building political transparency tools with RAG, or designing motion control systems, I'm driven by the belief that technology should empower and create positive change.
Technical Project Manager • Wat.AI Research
Leading development of reinforcement learning agents for autonomous renewable energy optimization in rural Sub-Saharan Africa. Integrated NASA POWER API for real-world weather simulation.
First Author • RAG Architecture
Developed an AI-powered political chatbot using RAG (Retrieval Augmented Generation) for accurate legislative Q&A. Led webscraping, architecture design, and evaluation methodology as first author on research paper.
Solo Project • Natural Language Scheduling
Built an intelligent course scheduling system for Waterloo Engineering students using natural language processing. Allows students to build their schedules conversationally by expressing interests and requirements, powered by LLMs.
Embedded Systems • Motion Control
Demonstrates motion control and sensor processing in a Qt/C++ application. Emulates a two-axis stage with linear and circular moves, synthetic laser displacement readings with moving average filtering, and automated calibration stored in SQLite.
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