👋🏼 I am an undergraduate student at the University of British Columbia (UBC) interested in Machine Learning/Artificial Intelligence and Optimization. I have worked as a Research Assistant (RA) applying AI to many fields and as a software engineer. I am passionate about understanding how large neural networks work to develop safe and interpretable AI.
"AML-TN sponsored internships highlight the value of developing young researchers as the next generation of machine learning specialists."
"These awards subsidize professors to hire international undergraduate students to work full-time on their research projects in the Summer Session (May to August)."
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Bringing safe and interpretable AI into medicine. We build software that ingests medical guidelines and delivers evidence-based recommendations through natural language interfaces, with privacy preserved. Our work combines computer science, medical, and clinical research in collaboration with UBC’s Cloud Innovation Centre. Current projects include agentic NLP pipelines with UIs hosted on AWS chatbots for Bipolar Disorder and Depression. Supervised by Dr. John Jose Nunez.
Working with SCARP and Electrical and Computer Engineering to use large language models (LLMs) to analyze public records from Vancouver's housing development approval process. The work bridges AI and social science to address Canada's housing crisis. Supervised by Dr. Julia Harten and Dr. Christos Thrampoulidis
Working with the faculty of arts to develop modules in existing faculty of arts courses that highlight how AI can be used in their field. For example, sequence modelling in economics or computer vision in archeology. Funded by UBC's Teaching and Learning Enhancement fund (TELF). Supervised by Dr. Laura Nelson and Dr. Jonathan Graves. Find more information here.
Applied AI-based drug discovery tools like AlphaFold and ProteinMPNN to optimize sequences and 3D structures of enzymes. Revamped automation systems for bioprocess engineering, orchestrating sensors, pumps, motors, and valves.
Developed particle tracking software using an ensemble of open-source computer vision models along with a UI to correct mistakes. Automated and developed data collection software for new instruments invented by the research group. Helped with some day-to-day lab activities. Supervised by Dr. John Frostad
President: Leading initiatives to encourage student understanding and future pathways in AI and ML.
Leading the ML sub-team to explore and tune open-sourced models for object detection and tracking. This is a small piece of the puzzle on our drones that compete in two university-level autonomous drone competitions every year.
Automated brewing with IoT-controlled devices while also trying to make glow-in-the-dark beer using green fluorescent protein (GFP).
Provided technical assistance to faculty, staff, and students for tech-related queries and equipment across campus.