👋 Welcome to the BayesOptApp GitHub organization! Our mission is to advance Bayesian Optimization (BO), drawing inspiration from real-world applied challenges. At the moment, we focus on tackling the complexities of high-dimensional search spaces, constrained optimization, mixed-variable domains, and other complex problem settings where standard BO struggles.
BayesOptApp is led by Dr. Elena Raponi, Assistant Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. This organization would not be possible without the contributions of Ivan Olarte Rodriguez, Maria Laura Santoni, and the many students of Leiden University, who take on the challenge of gettinf familiar and advancing BO within their thesis projects.
We aim to bridge the gap between theory and practice, developing new methodologies while also demonstrating their impact on engineering design, optimization under uncertainty, and complex real-world applications.
This organization hosts repositories for both:
- Fundamental research on novel Bayesian Optimization methodologies.
- Application-driven studies solving practical optimization challenges.
(There's a lot happening behind the scenes! Stay tuned for more!)
Investigating the performance of BO algorithms in high-dimensional spaces, comparing state-of-the-art methods, and understanding their scalability.
Leveraging BO to optimize material and structure designs without explicit analytical models, crucial for engineering applications.
We welcome collaborations and contributions! If you're interested in Bayesian Optimization, real-world optimization challenges, or applying BO to engineering design, feel free to explore our repositories and reach out to us.
