2026-08-12 –, Room 2
Hyperparameter optimization is a core workflow in machine learning and scientific computing, yet the Julia ecosystem has lacked a mature, production-ready framework comparable to the robust, battle-tested tools available in other languages. In order to bridge this gap, we present Optuna.jl a package that brings the full functionality of Optuna (by Preferred Networks, Inc.), one of the most widely adopted hyperparameter optimization frameworks, into Julia.
Hyperparameter optimization is essential for improving model performance, robustness, and generalization across machine learning, simulation, and data-driven applications. While Python users have long benefited from mature frameworks like Optuna [1], Google Vizier [2], and Weights & Biases [3], Julia's native offerings remain limited: the most established package, Hyperopt.jl, is in maintenance mode and no longer accepts new features, and to our knowledge no existing Julia package supports distribution across multiple machines.
This talk presents Optuna.jl, which can be used to seamlessly integrate the Optuna framework into your Julia code via CondaPkg.jl and PythonCall.jl, and optimize hyperparameters directly in Julia.
We support all samplers, pruners and database backends, with native single-threaded, multi-threaded and multi-process execution and took special care to make sure high-performance Julia-Code doesn't get slowed down from Python-Calls.
We will also introduce OptunaDashboard.jl, a wrapper for the optuna-dashboard package for visualizing and managing optimization studies.
“Optuna, the Optuna logo and any related marks are trademarks of Preferred Networks, Inc.”
[1] Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[2] Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., Sculley, D. (2017). Google Vizier: A Service for Black-Box Optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017 (pp. 1487–1495). ACM.
[3] Biewald, L. (2020). Experiment Tracking with Weights and Biases. https://www.wandb.com/
Research scientist @ University of Augsburg, chair of mechatronics
Github:
- JulianTrommer
- Chair of Mechatronics
Lars Mikelsons holds a diploma in Mathematics and a Ph.D. in Mechatronics. He began his professional career at Bosch Corporate Research before transitioning to academia. Currently, he is the Head of the Chair for Mechatronics at the University of Augsburg. His research focuses on Scientific Machine Learning and Mechatronic Systems Engineering, contributing to the advancement of intelligent, data-driven approaches in engineering applications.