Eugene Kwan
Associate Principal Scientist
Cambridge, Massachusetts, United States
Summary
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Senior🎓
Top SchoolEugene Kwan is an Associate Principal Scientist based in Cambridge, MA, with 11 years of experience applying high-throughput experimentation, lab automation, and data-driven methods to solve mechanistic problems in organic chemistry. At Merck he leads data-rich experimentation combining ML and computational chemistry with NMR and chromatography to accelerate discovery, building on a Harvard PhD and postdoctoral work in ultrasensitive kinetic isotope effect measurements. He bridges hands-on experimental skills—microfluidics, genomics, and HTE—with computational tools (DFT, mechanistic modeling) to extract causal insight rather than just predictive correlations. Known for integrating analytical enabling technologies into discovery workflows, he pairs deep spectroscopy expertise with practical automation to scale reproducible experiments. An unusual strength is translating subtle mechanistic observables (e.g., KIEs and dynamical NMR shifts) into features that improve machine-learning-guided reaction optimization.
11 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Chemistry, Doctor of Philosophy - PhD, Chemistry at Harvard UniversityBachelor of Science - BS, Chemistry, Bachelor of Science - BS, Chemistry at University of Toronto
Github Skills (76)
flask10
plotly10
jupyter10
finance10
bioinformatics10
gaussian10
modeling10
julia9
data-visualization9
plotly-dash9
topological-data-analysis9
charting9
python9
no-javascript9
prediction9
Programming languages (6)
Github contributions (5)
ekwan/Jprogdyn
Oct 2018 - Apr 2020
Quasiclassical and classical dynamics with Gaussian
Contributions:42 commits, 22 pushes, 2 comments in 1 year 6 months
gaussianentropystate-spacedynamics
ekwan/PyQuiver
Aug 2016 - Jul 2022
kinetic isotope effect prediction with Gaussian
Contributions:1 review, 54 commits, 4 PRs in 6 years
kineticgaussianpredictionisotope
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