Emre Sevgen is a Machine Learning Lead and molecular engineer with a decade of experience applying mathematics, computation and deep learning to accelerate molecular simulations and design. He develops novel ML algorithms—especially recurrent networks and autoencoders—for enhanced sampling and dimensionality reduction, and has productionized models for protein and materials engineering at companies like Evozyne and Citrine. His background spans high-performance scientific software (co-leading SSAGES development), patent-pending deep-learning chemical tools, and academic PhD research extracting collective motions from DNA simulations. Based in Chicago, he blends research-grade algorithm development with practical engineering that has delivered 50x pipeline speedups and engineered artificial proteins for sustainability and healthcare. Notably, he pairs domain expertise in molecular engineering with hands-on ML systems work, making him effective at moving methods from theory into applied therapeutics and materials workflows.
9 years of coding experience
14 years of employment as a software developer
Bachelor of Science (BS), Chemical and Biomolecular Engineering, Bachelor of Science (BS), Chemical and Biomolecular Engineering at University of Illinois Urbana-Champaign
Doctor of Philosophy (Ph.D.), Molecular Engineering, Doctor of Philosophy (Ph.D.), Molecular Engineering at University of Chicago
Simulations of dice rolls for damage curves etc. in DnD 5e
Contributions:2 PRs, 23 pushes, 5 branches in 3 months
rollsdice-rollssimulationssimulationdamage
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Emre Sevgen - Machine Learning Lead at Avista Therapeutics