Sepehr Sameni
Research Engineer at NVIDIA
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolSepehr Sameni is a Research Engineer based in Zurich with 10 years of experience bridging academic research and applied ML; he recently joined NVIDIA after completing a PhD in Computer Science at the University of Bern where he focused on self‑supervised representation learning for computer vision. His research interests include video generation and multi‑modal (text‑image) representation learning, complemented by an Adobe research internship that sharpened his applied research skills. An active open‑source contributor, he has improved tensorflow/tensor2tensor and maintains a curated awesome‑sentence‑embedding repo that automates Semantic Scholar pulls and README generation, demonstrating a taste for reproducible tooling. Sepehr combines rigorous experimental practice with production-minded model refinement, making him effective at turning novel representation learning ideas into practical systems.
10 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
Github Skills (19)
markdown10
python10
machine-learning10
document-generation10
tensorflow210
deep-learning10
markdown-it10
tensorflow10
nlp10
github-api9
transformer-models9
data-analysis8
api8
apim8
json8
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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