Jessy Lauer
Postdoctoral Researcher at EPFL (École polytechnique fédérale de Lausanne)
Geneva, Geneva, Switzerland
Summary
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Rockstar🎓
Top SchoolJessy Lauer is a postdoctoral researcher at EPFL with six years of experience at the intersection of biomechanics and machine learning. With a PhD in Biomechanics from Universidade do Porto and postdoctoral work at Harvard, she brings rigorous experimental and computational expertise to movement analysis. Previously a biomechanist at Kinetikos Health, she translates real-world human movement problems into robust analytical solutions. As a contributor and back-end ML engineer to the popular DeepLabCut project, Jessy improved multi-animal tracking, tracking algorithms, and data-validation tooling for a widely used markerless pose-estimation library. Her work uniquely blends domain knowledge in sport sciences with production-oriented ML development, enabling reproducible, user-friendly tools for the research community. Based in Geneva, she combines academic rigor with practical software contributions that accelerate behavioral and biomechanical research.
6 years of coding experience
Postdoc, Postdoc at Harvard UniversityMaster of Science - MS, Sport Sciences, Master of Science - MS, Sport Sciences at Norwegian School of Sport Sciences (NIH)Doctor of Philosophy - PhD, Biomechanics, Doctor of Philosophy - PhD, Biomechanics at Universidade do Porto
Github Skills (11)
algorithm10
pandas10
computer-vision10
algorithms10
machine-learning10
deep-learning10
tensorflow10
python10
data-analysis10
front-end-development9
video-processing9
Programming languages (2)
Github contributions (5)
DeepLabCut/DeepLabCut
Jan 2020 - Jan 2023
Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:194 reviews, 534 commits, 302 PRs in 3 years
Contributions summary:Jessy primarily contributed to the refinement and improvement of the DeepLabCut codebase, focusing on enhancing the machine learning pipeline. Their work included addressing index errors in the training network GUI and ensuring video validity checks during analysis. They also implemented features for multi-animal tracking, improved the tracking algorithm, and added options for users to manipulate and analyze the keypoint data for the project.
pytorchkeypoint-detectiondefinedmarkerlessdeep-learning
DeepLabCut/napari-DLCLabelingAlpha
Feb 2022 - Dec 2022
Contributions:56 commits, 2 PRs, 7 pushes in 9 months
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