Dakota Folmsbee
Senior Data Scientist
Newark, Delaware, United States
Summary
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Senior🎓
Top SchoolDakota Folmsbee is a Senior Data Scientist and computational chemist with nine years of experience applying machine learning to drug discovery, materials science, and molecular modeling. With a PhD from the University of Pittsburgh, Dakota has advanced ML representations and physics-informed modeling to predict molecular and materials properties, and transitioned those methods into industry roles optimizing concrete mixes and biomineralization forecasts. He combines hands-on simulation expertise (CHARMM, AMBER, GROMACS, WESTPA) with production-focused tooling—authoring a Python molecular representation library used for chemistry ML workflows and building data infrastructure and visualization tools. Dakota’s work spans inverse materials design with genetic algorithms to pharmacophore-based hit discovery, showcasing a rare blend of algorithmic rigor and experimental-awareness. Based in Newark, Delaware, he moves smoothly between academic research and deployed R&D, often improving data pipelines and conformer strategies that materially boost model inputs. Colleagues describe him as a scientist-engineer who translates chemical physics into practical AI solutions that accelerate materials and drug development.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Physical Chemistry, Doctor of Philosophy - PhD Physical Chemistry at University of PittsburghBachelor's Degree Chemistry, Bachelor's Degree Chemistry at Clarkson University
Github Skills (30)
representations10
materials-informatics8
chemistry7
binder6
cheminformatics6
machine-learning6
benchmarking6
benchmark6
sequential5
toolbox5
infrared5
genetic4
optuna4
datasets4
python4
Programming languages (4)
Github contributions (5)
chemreps/chemreps
Aug 2018 - Nov 2020
Implementation of various machine learning representations for molecules
Contributions:3 releases, 1 review, 45 commits in 2 years 3 months
moleculesrepresentationschemoinformaticsmachine-learningrepresentations-learning
dlf57/chemistry
Nov 2018 - Mar 2021
Information about the various fields within chemistry
Contributions:5 PRs, 5 pushes, 4 branches in 2 years 4 months
chemistryfields
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