Summary
Garyk Brixi is a student researcher and computational biologist with eight years of experience applying machine learning to biological problems, currently conducting genome-wide association studies at Harvard Chan School. He has hands-on expertise building and interpreting convolutional neural networks for neural data and reimplementing published models in TensorFlow and PyTorch, linking computational models to vision science. His background spans industry and research internships—from scalable workflow tooling at the Federal Reserve Bank of Boston to deploying predictive systems and recommendation engines in production at startups. Garyk founded a social-impact venture that raised $52,000 to pilot optimized malnutrition treatments in Malawi and led treatment development at Valid Nutrition, where his ML-driven logistics models demonstrated meaningful cost savings. He combines wet-lab adjacent genomics experience (prime editing pipelines at the Broad Institute) with practical software delivery (Floki web app, Flask deployments), making him effective at translating research questions into deployable tools. Notably, he repeatedly improves model performance through custom loss functions and automation, showing a bias toward measurable impact and reproducible pipelines.
7 years of coding experience
2 years of employment as a software developer
Chinese, Czech, English