Nicolas Hug, a software engineer who specializes in machine learning, has been promoted to an Associate Research Scientist at the Data Science Institute (DSI).

Hug joined DSI in July 2018 as a Postdoctoral Researcher, helping Andreas Mueller maintain the scikit-learning machine-learning library. He excelled at that work and in April was approved by the scikit-learn developers to become a core developer. For the last year, his work has improved several aspects of the library. 

Now, in his new position as an Associate Research Scientist at DSI, Hug will continue to maintain the library with the added benefit that he can be listed as a principal investigator on grant applications. In fact, he and Mueller recently submitted a grant to the Chan Zuckerberg Initiative whose intent is to further maintain scikit-learn.

“Nicolas [Hug] has been extremely dedicated in contributing to the scikit-learn project and engaging with the community,” says Mueller, an Associate Research Scientist at DSI who also teaches machine-learning courses. “He is an active participant in designing the future of the project and has made far-reaching contributions to the code.”

Core developers review code contributions, merge approved pull requests, and guide the development of the library by weighing in on major changes to the application-program interface. Hug focused on integrating automatic machine learning tools to scikit-learn and helped with code maintenance and code reviews. He also improved the gradient-boosting models and helped users who had questions or needed guidance.

Hug, 28, grew up in Paris. He graduated from The Institut National des Sciences Appliquées of Toulouse, a French engineering university, where he studied computer science and critical systems, especially software engineering. He did a research master’s degree at the University of Toulouse, focusing on machine learning, and later earned his doctorate from Toulouse. 

While working on his Ph.D., Hug created a Python scikit building and analyzing recommender systems called Surprise. Having that experience helped him get the job at DSI, where he assists Mueller, a prominent scikit-learn core developer and author of the popular book, Introduction to Machine Learning with Python. Rounding out the DSI scikit-learn team is Thomas Fan, a core developer and talented software developer whose research has significantly enhanced the library. 

“It’s amazing to have a dedicated team at DSI working on scikit-learn,” says Mueller. Nicolas [Hug] and Thomas [Fan] have both been tremendously valuable to the scikit-learn community and continue to speed up development, improve community engagement, and of course, implement new features. The Data Science Institute is in a unique position in data science to support a core project like scikit-learn on this scale.”

— Robert Florida