Summary
Chris Lin is a Computer Science PhD student at the University of Washington with a decade of hands-on experience applying statistics, causal ML, and deep learning to biomedical problems. He has bridged academia and industry—contributing to cancer detection and EHR research at GRAIL and Stanford, building GNNs and explainable graph methods for drug discovery at AstraZeneca, and deploying causal and VAE models for single-cell data at GSK. A recent research intern at Microsoft, Chris combines a strong statistical foundation (MS and BA in Statistics) with practical engineering in PyTorch and PyTorch Geometric to move models from prototype to validated results. He regularly collaborates with clinicians and international research teams, bringing domain-aware modeling and reproducible analysis pipelines to large-scale biomedical studies. Notably, his work includes a published method for explainable GNNs and production-style frameworks for stable feature selection in high-stakes clinical settings.
10 years of coding experience
6 years of employment as a software developer
Bachelor of Arts (B.A.), Statistics, Bachelor of Arts (B.A.), Statistics at University of California, Berkeley
Master of Science (M.S.), Statistics, Master of Science (M.S.), Statistics at Stanford University
English, Chinese