Duc Trung is a Technical Director and open-source developer with 7 years of experience building and shipping developer-facing tools in the Jupyter ecosystem and ML platforms. Based in Greater Paris, he combines a PhD-trained, research-first mindset with hands-on full‑stack and backend engineering—contributing to flagship projects like JupyterLab, JupyterHub and Voilà and improving debugger UX, service management, and progressive rendering. He has also worked on cloud and local ML tooling, enhancing SageMaker's local Docker training and runtime for smoother developer workflows. Known for pragmatic refactors, UI polish, and robust testing (including Playwright benchmarks), he bridges scientific computing and production-grade web technologies. As a Jupyter Distinguished Contributor and leader at QuantStack and Notebook.link, he focuses on making notebook sharing and interactive computing scalable and user-friendly.
7 years of coding experience
7 years of employment as a software developer
Master of Science (M.Sc.), Mechanics of Soils, Rocks and Structures in their Environment (MSROE), Master of Science (M.Sc.), Mechanics of Soils, Rocks and Structures in their Environment (MSROE) at Ecole Nationale des Ponts et Chaussées
Bachelor of Engineering (B.Eng.), Civil Engineering, Bachelor of Engineering (B.Eng.), Civil Engineering at National University of Civil Engineering, Hanoi, Vietnam
Doctor of Philosophy (Ph.D.), Mechanical Engineering, Doctor of Philosophy (Ph.D.), Mechanical Engineering at Pierre and Marie Curie University
Voilà turns Jupyter notebooks into standalone web applications
Role in this project:
Front-end Developer
Contributions:3 releases, 140 reviews, 148 commits in 1 year
Contributions summary:Duc primarily focused on enhancing the front-end aspects of the Voila dashboarding project. Their contributions included integrating JavaScript render factories from `@jupyterlab/javascript-extension`, creating benchmark helpers for UI tests using Playwright, and adding new tests. These changes indicate a focus on improving the user interface, performance testing, and overall user experience of the Voila dashboards. The user also made a series of commits to support progressive rendering.
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
ML Engineer
Contributions:12 reviews, 17 PRs, 41 comments in 1 year 1 month
Contributions summary:Duc significantly contributed to the local mode configuration within the SageMaker Python SDK, enabling the use of Docker containers for local training and deployment. They implemented features to configure Docker containers, including the addition of arguments, configuration files, and associated tests. Furthermore, the user updated the local SageMaker runtime client and session, ensuring the smooth functioning of local mode functionalities. They also addressed issues related to non-Python files in job dependencies.
pytorchsagemakerdeployingmxnetpython
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