ML Research Intern @ University Health Network
Conducting machine learning modeling and data analysis to advance research on liver transplantation outcomes and care.
I am a student researcher at McMaster University focused on health-tech and human–AI interaction. I build practical, safety-minded machine learning systems for clinical use and collaborate closely with clinicians to design dependable, deployable solutions. I am seeking internship opportunities for Summer 2026 in machine learning, health technology, or research.
Outside of research, I have been a pianist for 12 years. I also enjoy blogging, running marathons, reading philosophy, making short films, and traveling.
Experience
AI Team Lead @ MED-T
Led artificial intelligence initiatives and provided mentorship to student teams working on innovative medical technology development projects.
ML Research Student @ University Health Network
Built automated algorithms for aortic imaging analysis and performed comprehensive biomechanical data evaluation.
Director (Biomedical Hackathon) @ MED-T, McMaster
Co-organized Canada's largest biomedical hackathon with over 500 participants and managed strategic partnerships.
Research Assistant @ University of Toronto
Conducted research on environmental interactions and investigated the impact of pharmaceutical compounds on MRSA bacterial resistance.
Projects
ApneaNet –– Predictive Clinical Modeling
Collaborated in a multidisciplinary team to build a machine learning model for diagnosing sleep apnea from real-world clinical data; Winner of Google x Temerty Medicine Datathon 2025.
1st Place @ Toronto Health Datathon 2025
Baymax –– AI for Diagnosis
ECG-based arrhythmia detection system using machine learning algorithms for real-time cardiac monitoring and early warning detection.
Winner @ HackTheHeart 2024
Hero –– Revitalizing Health
Assistive device designed for stroke patients to improve mobility and rehabilitation through innovative technology and user-centered design.
First Place @ Hackathon
SepsisAI –– AI Sepsis Prediction
Machine learning system for predicting patient deterioration and early sepsis detection in collaboration with Google Health.
In collaboration with Google