I'm an Honors Computing Science student at the University of Alberta, deeply interested in systems-driven software engineering, machine learning infrastructure, and developer tooling. I build projects that simulate real-world production challenges—where correctness, observability, and simplicity matter.
I’m actively looking for engineering internships and research opportunities where I can contribute to impactful products and learn from principled engineers.
Languages:
Python, Java, C/C++, JavaScript, TypeScript, SQL, MongoDB, Bash, R
Frameworks:
PyTorch, TensorFlow, Node.js, React Native, Streamlit, LangChain
Developer Tools:
Git, Docker, Firebase, AWS, GCP (Vertex AI, Translation API), PyTest
Libraries:
pandas, NumPy, FAISS, Matplotlib, SDL2, shutil
Environments:
Linux/UNIX, Windows
Python, FastAPI, scikit-learn, Docker, pytest
A production-style recommendation API that serves real-time product suggestions using implicit purchase signals. Supports collaborative filtering and hybrid (CF + embedding) inference with a typed, test-covered backend and Dockerized deployment.
Python, LangChain, Vertex AI, Google Translate, FAISS
An end-to-end multilingual customer support chatbot for 100+ languages using translation → retrieval → generation → re-translation. Includes a robust vector DB setup, language detection, and automated test harnesses for reliability.
Android, Firebase, Java, Node.js
A mobile-first event management platform with lottery-based waitlist handling, contextual notifications, and QR-based check-in. Used Firebase Cloud Functions to automate selection logic and reduce organizer overhead.
React Native, TypeScript, WebSockets
A real-time mobile experience that animates a plant avatar using live biosensor data. Built during natHacks; integrated PL@NT.net ML API for species detection and emotional state rendering.
- Distributed ML tooling and embedding stores
- Scalable APIs with observability and graceful fallbacks
- Practical applications of AI-assisted development workflows