Yiyang Wang
Graduate Research Assistant @BC Cancer
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WORK HISTORY
Graduate Research Assistant @BC Cancer
Vancouver, BC, CA
Developing a self-supervised graph neural network pipeline to learn tissue-level embeddings from spatial transcriptomics data—without relying on matched scRNA-seq, annotations, or predefined labels.
EDUCATION
The University of British Columbia
Master of Science - MS
McGill University
Bachelor of Applied Science - BASc
ABOUT YIYANG WANG
Hi, I’m Yiyang — a Master’s student in Bioinformatics at UBC with a background in biology, mathematics, and a deep interest in how AI can unlock new solutions in medicine. Right now, I’m working on my thesis in the AI in Medicine Lab, where I’m building self-supervised graph neural networks to analyze spatial transcriptomics data — no labels, no annotations, just learning structure from the data itself. I love these kinds of problems: where biology is messy, data is rich, and machine learning can help make sense of it all. Before UBC, I studied Biology and Math at McGill and worked on a range of research projects — from chemotherapy resistance and transposable elements to cell segmentation in microscopy images and prion-like protein evolution. I’ve also spent time in industry at Roche, developing pipelines to analyze cancer genomics data for real clinical impact. What connects all of this is a fascination with how complex systems — like cells, tissues, or genomes — organize themselves, break down, and respond to change. I’m especially drawn to research at the edge of computation and biology, where the tools are evolving just as fast as the questions. If you’re working on something interesting in computational biology, ML in healthcare, or just want to talk science, feel free to reach out.
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