Guillaume Lambard
Senior Researcher at National Institute for Materials Science
Ibaraki Prefecture, Japan
Summary
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Senior🎓
Top SchoolGuillaume Lambard is a senior researcher based in Ibaraki, Japan, specializing in applying AI to accelerate discovery and design of materials—from small organic molecules and polymers to inorganic crystals and alloys. With a PhD in particle and theoretical physics and eight years of applied experience, he builds ML, NLP, GAN and Bayesian active-learning tools that predict electronic, optical and macroscopic properties orders of magnitude faster than experiments or traditional simulations. At the National Institute for Materials Science he couples property predictors with a bespoke molecular generator to auto-design compounds with target behaviors and is extending this approach toward drug discovery for hard-to-treat diseases. His background in statistical mathematics and cheminformatics gives him a rare combination of rigorous theory and practical model-driven design, enabling end-to-end workflows from prediction to candidate generation. Known for bridging physics intuition with modern AI, he often uncovers nonobvious structure–property relationships that guide experimentalists toward higher-value targets.
8 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Particle Physics, Theoretical and Mathematical Physics, Doctor of Philosophy - PhD, Particle Physics, Theoretical and Mathematical Physics at Aix-Marseille University
French, English, Japanese
Github Skills (27)
compounds10
structural-biology9
docking9
theano9
proteins9
computational-biology9
optimizer8
datasets8
keras-tensorflow8
keras7
medical7
python7
drug-discovery7
web-crawler6
tensorflow5
Programming languages (2)
Github contributions (5)
GLambard/AdamW_Keras
Jul 2018 - Aug 2019
AdamW optimizer for Keras
Contributions:7 commits, 1 PR, 6 pushes in 1 year 1 month
deep-learningadamwtheanoneural-networkskeras-tensorflow
GLambard/SMILES-X
Oct 2018 - Apr 2020
Autonomous characterization of molecular compounds from small datasets without descriptors
Contributions:1 release, 86 commits, 5 PRs in 1 year 5 months
characterizationproteinspythonautonomousdocking
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