Thibault Séjourné
Quantitative Researcher
Lausanne, Vaud, France
Summary
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Senior🎓
Top SchoolThibault Séjourné is a quantitative researcher and postdoctoral AI fellow with eight years of experience bridging applied mathematics and machine learning, currently working at Qube Research & Technologies after a postdoc at EPFL. He earned a PhD from ENS Paris focused on optimal transport and non-convex optimization, and has led and co-authored collaborative research that reached top-tier venues. His work spans incorporating physical priors into ML, fine-tuning foundation models, and practical quantitative problems—combining rigorous math with production-minded modeling. He has supervised students, revived stalled collaborations into publications, and regularly presents at conferences, signaling both mentorship and sustained scholarly impact. Based in Lausanne, he brings a rare mix of academic depth and industry application, with early experience in lean manufacturing and systems modeling that informs his pragmatic approach to complex black-box systems.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computational and Applied Mathematics, Doctor of Philosophy - PhD, Computational and Applied Mathematics at Ecole normale supérieureClasses Préparatoires aux Grandes Ecoles, Classes Préparatoires aux Grandes Ecoles at Louis Le GrandApplied Mathematics, Third Year, Applied Mathematics, Third Year at Ecole polytechniqueMaster of Science - MS MVA (Mathématiques, Vision, Apprentissage), Computer Vision, Applied Mathematics, Master of Science - MS MVA (Mathématiques, Vision, Apprentissage), Computer Vision, Applied Mathematics at École Normale Supérieure Paris-Saclay
French, English, Spanish, Chinese
Github Skills (14)
scikit-learn10
statistics10
machine-learning10
python10
data-science10
data-analysis10
backend9
pytorch6
pot6
numerical-optimization5
algorithm3
loss-functions3
cyclegan2
automatic-differentiation1
Programming languages (2)
Github contributions (5)
thibsej/unbalanced_gromov_wasserstein
Jun 2020 - Dec 2022
Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
Contributions:72 commits, 65 pushes, 2 comments in 2 years 6 months
thibsej/unbalanced-ot-functionals
Nov 2019 - Dec 2022
It is a repo which allows to compute all divergences derived from the theory of entropically regularized, unbalanced optimal transport. It relies on a pytorch backend.
Contributions:49 commits, 37 pushes, 1 comment in 3 years 1 month
backendpytorch
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