Jonathan Geuter
United States
Summary
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Rockstar🎓
Top SchoolJonathan Geuter is a PhD student in Applied Mathematics at Harvard and an AI research intern at MBZUAI’s Institute of Foundation Models, bringing five years of research and engineering experience across academic and industry settings. His work sits at the intersection of optimal transport, generative modeling, and statistical machine learning, with hands-on experience training foundational language embedding models during an internship at Jina AI. Jonathan has a strong theoretical foundation from TU Berlin and practical research exposure from Zuse Institute Berlin and UC Berkeley, and he’s comfortable moving between proofs, Python-based teaching, and model training pipelines. Colleagues describe him as someone who pairs mathematical rigor with a practical bent for scalable ML systems—often exploring how principled transport methods can improve generative models.
4 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Bachelor of Science - BS at University of California, BerkeleyBachelor of Science - BS, Bachelor of Science - BS at Technische Universität Berlin
English, German, French, Italian, Chinese, Spanish
Github Skills (25)
frank10
optimization10
openai-clip9
gradient9
metric-learning9
siamese-network9
julia9
jina9
transfer-learning8
graph-convolutional-networks8
graph-neural-network8
deep-learning8
medicine8
few-shot-learning8
graph8
Programming languages (3)
Github contributions (5)
j-geuter/FrankWolfe.jl
Jun 2021 - Nov 2022
Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Contributions:2 PRs, 132 pushes, 28 branches in 1 year 4 months
frank-wolfeconditionalfrankjuliagradient
j-geuter/SinkhornNNHybrid
Sep 2022 - Jan 2023
Initializing the Sinkhorn Algorithm using a Neural Network
Contributions:286 commits, 2 PRs, 240 pushes in 4 months
neural-networksinkhornsinkhorn-algorithmmachine-learning
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