Sepehr Sameni
Research Engineer at NVIDIA
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolSepehr Sameni is a research engineer and Computer Science PhD with 11 years of experience, now at NVIDIA after completing a PhD at the University of Bern focused on self-supervised representation learning for computer vision. He has hands-on experience in multi-modal and video generation research, and interned at Adobe working on CLIP-like models under senior researchers. Beyond academia, he contributes to open-source ML tooling—helping maintain tensorflow/tensor2tensor and curating a popular repository of sentence and word embeddings—demonstrating a strong blend of research rigor and practical engineering. Based in Zurich, he pairs deep theoretical knowledge with production-minded code refinement and dataset tooling, often improving reproducibility and developer workflows.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of BernMathematics and Physics, Mathematics and Physics at Allameh HelliHelli 2 elementary schoolMaster's degree, Artificial Intelligence, Master's degree, Artificial Intelligence at University of Tehran
English, Persian, German
Stackoverflow
1,276reputation
206kreached
24answers
6questions
pytorch
top-5%
Github Skills (25)
markdown10
python10
machine-learning10
document-generation10
deep-learning10
markdown-it10
tensorflow10
nlp10
pytorch9
github-api9
transformer-models9
data-analysis8
api8
json8
apim8
Programming languages (8)
Github contributions (5)
Separius/awesome-sentence-embedding
Dec 2018 - Apr 2021
A curated list of pretrained sentence and word embedding models
Role in this project:
ML Engineer
ML EngineerContributions:198 commits, 15 PRs, 173 pushes in 2 years 4 months
Contributions summary:Sepehr primarily contributed to the development and maintenance of tools for generating and displaying sentence and word embedding models. Their work includes creating scripts to fetch data from semantic scholar, generate markdown tables for the README, and incorporating GitHub star counts. The user also focused on creating tables for contextualized and encoder models, demonstrating a focus on various aspects of embedding models. Furthermore, they improved the generation of the README.md file by adding new features and updating its structure.
nlpsentenceword-embeddingssentence-embeddingssentence-similarity
tensorflow/tensor2tensor
Jul 2019 - Nov 2019
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Role in this project:ML Engineer
ML EngineerContributions:6 commits, 3 PRs in 3 months
Contributions summary:Sepehr primarily contributed to the maintenance and improvement of the TensorFlow-based machine learning models within the repository. Their work involved correcting typos, refactoring code, and updating parameters related to attention mechanisms. They also focused on testing and debugging, including ensuring the correct use of batch sizes and shapes within the models. These changes suggest a focus on the refinement and testing of machine learning model implementations.
pytorchautoencoderdeep-learningmachine-translationreinforcement-learning
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