Erol Kavvas is a machine learning scientist in San Diego with over a decade of experience building end-to-end ML systems that integrate AI, networks, and large-scale multi-omics to make biology more predictive. He has a PhD in Bioengineering (systems biology) and a track record of high-impact research—four first-author papers including two in Nature Communications and 900+ citations—plus applied roles deploying models for human and microbial biology across academia and industry. His work spans transcriptomics to fluxomics, creating physics-informed hybrid mechanistic-ML frameworks that both improve interpretability and generate experimentally testable hypotheses. At Envisagenics and DELFI Diagnostics he translated genomics and splicing insights into production-grade models, including LLMs, while earlier projects delivered personalized diet recommendation systems and metabolomics-driven drug-response pipelines. Known for bridging rigorous research and practical deployment, he often focuses on making complex multi-modal data actionable for translational biology.
10 years of coding experience
9 years of employment as a software developer
Bachelor's degree Civil and Environmental Engineering, Bachelor's degree Civil and Environmental Engineering at University of California, Davis
Contributions:8 commits, 7 pushes, 1 branch in 1 month
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Erol Kavvas - Machine Learning Scientist at DELFI Diagnostics