Stefano Cereda is a Senior Data Scientist at Akamas with 11 years of experience and a PhD in Information Technology from Politecnico di Milano, where his industrial fellowship focused on automatic tuning of IT system configurations. He brings deep expertise in machine learning and optimization, translating research into production-ready solutions for system tuning and performance. An active open-source contributor, he has contributed bug fixes, tests, and documentation to scikit-optimize, improving Bayesian optimization routines like gp_minimize and categorical space handling. Stefano is passionate about GNU/Linux and pragmatic tooling, favoring reproducible, test-driven development and clear examples for users. Based in Lombardy, Italy, he blends academic rigor with hands-on engineering at the intersection of ML and systems optimization.
11 years of coding experience
Doctor of Philosophy - PhD, Information Technology, Doctor of Philosophy - PhD, Information Technology at Politecnico di Milano
Sequential model-based optimization with a `scipy.optimize` interface
Role in this project:
ML Engineer
Contributions:8 commits, 9 PRs, 37 comments in 2 years 4 months
Contributions summary:Stefano contributed to the `scikit-optimize` project, a library for Bayesian optimization. Their work included fixing bugs related to optimization, particularly in the `gp_minimize` function and categorical space handling. They also added tests and modified existing ones, ensuring the robustness of the optimization algorithms. Furthermore, they made updates to documentation and examples, such as the parallel optimization notebook.
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.