About

I am a Machine Learning Applied Scientist at Prenuvo, where I leverage deep learning and computer vision to advance organ health assessment from MRI scans, with a primary focus on the spine. I have contributed to the development of the FDA-cleared AI-powered Prenuvo body composition report which is now available to individuals undergoing scans at Prenuvo clinics. With a Master of Applied Science in Electrical and Computer Engineering from UBC, I specialize in graph neural networks, multiple-instance learning, and domain adaptation for medical image analysis. During my research at UBC’s Robotics and Control Laboratory, I developed innovative methods for prostate cancer classification and risk stratification, integrating histopathology image analysis with clinical data to improve patient outcomes. My work has been published in leading AI and medical imaging journals. Beyond the technical expertise, I have a strong passion for mentorship and education, having mentored UBC Medicine datathon and taught machine learning and robotics courses as a graduate teaching assistant at UBC. I’m always open to connecting with like-minded individuals, so feel free to reach out to me on LinkedIn if you’d like to start a conversation.