The goal of optimal design is to make the most efficient use of limited resources (such as time, budget, or available experiments) to achieve specific objectives, such as building accurate models, estimating parameters, or minimizing uncertainty. Our lab focuses on the application of sequential decision algorithms and optimal learning in experimental intelligence. We are engaged in developing methods that improve decision-making processes through iterative cyber-physical experimentation and learning. Our approach is based on adapting and refining strategies based on previous outcomes, enhancing both efficiency and precision. Every experimental workflow is and can be mathematically formulated as a sequential decision problem with uncertainty. This work has practical applications in various areas, including 3D printing optimization and resource management. Our goal is to combine theoretical research with practical problem-solving, contributing to advancements in scientific understanding and offering solutions to real-world challenges.