Lorenzo Boninsegna is a computational chemist and machine learning postdoc with nearly a decade of experience building physics-informed and data-driven methods for biomolecular systems and drug discovery. He blends molecular dynamics, statistical mechanics, and ML to create platforms that scale from atomistic models to whole-genome 3D structure prediction, achieving >99% agreement with experimental Hi-C datasets. A PhD-trained theoretician with roots in mathematical physics, he has translated complex methods into reproducible software (see CompChemML on GitHub) and published in high-impact venues like Nature Methods. Awarded UCLA’s Chancellor’s Award and the 2024 J. Pickett Award, he excels at cross-disciplinary collaboration and communicating technical ideas to diverse audiences. Notably, he pairs a soft spot for statistical mechanics with practical generative and ligand-based modeling approaches that bridge theory and applied drug design.
10 years of coding experience
7 years of employment as a software developer
Master's degree Theoretical and Mathematical Physics, Master's degree Theoretical and Mathematical Physics at Università di Trento
Doctor of Philosophy - PhD Chemistry, Doctor of Philosophy - PhD Chemistry at Rice University
Diplom Physiker (B.S. + M.S.) Theoretical and Mathematical Physics, Diplom Physiker (B.S. + M.S.) Theoretical and Mathematical Physics at University of Tübingen
Contributions:3 PRs, 6 pushes, 1 branch in 3 years 7 months
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