Vincent Stimper
Senior Research Scientist at Isomorphic Labs
Lausanne, Vaud, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolVincent Stimper is a Senior Research Scientist at Isomorphic Labs with nine years of experience applying probabilistic modeling and representation learning to drug discovery and physical sciences. He holds a PhD-level research background from Cambridge and the Max Planck Institute, where he developed probabilistic methods and representation learning under leading supervisors. Vincent has industry experience at Microsoft and Amazon building generative models for molecular sampling and computer vision for robotics, and he brings hands-on expertise coding core ML components in PyTorch (notably a normalizing flows implementation). Based in Lausanne, he combines academic rigor with product-focused research to turn complex physical and molecular problems into tractable ML solutions. A less obvious strength is his track record of translating research prototypes into usable software (packagable Python libraries and scikit-image contributions), enabling broader adoption of his methods.
10 years of coding experience
6 years of employment as a software developer
PhD in Engineering, Machine Learing, PhD in Engineering, Machine Learing at University of CambridgeMaster's degree, Physics, Master's degree, Physics at Technical University of Munich
German, English, French
Stackoverflow
73reputation
3kreached
4answers
3questions
Github Skills (10)
pytorch10
deep-learning10
python10
modeling10
implement10
autoregressive-models9
linux6
memory-efficient6
tensorflow6
gpu6
Programming languages (6)
Github contributions (5)
VincentStimper/normalizing-flows
Jan 2020 - Jan 2023
PyTorch implementation of normalizing flow models
Role in this project:
Back-end Developer
Back-end DeveloperContributions:12 releases, 3 reviews, 620 commits in 3 years
Contributions summary:Vincent primarily focused on implementing the core functionality of the normalizing flow models using PyTorch. Their contributions included making the package installable by creating a setup script and a package initialization file. The user also implemented key flow components, such as the generic flow class, planar flow, and the overall framework of the normalizing flow model with multiple distributions.
pytorchnormalizing-flowvariational-autoencodervariational-inferencereal-nvp
lollcat/fab-torch
Nov 2021 - Jan 2023
Flow Annealed Importance Sampling Bootstrap (FAB). ICLR 2023.
Contributions:30 reviews, 211 commits, 48 PRs in 1 year 2 months
bootstrapannealed-importance-samplingboltzmann-distributionnormalizing-flowboltzmann-generator
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