Adaptive Design

Adaptive design optimizes experimental processes or system designs by iteratively adjusting parameters based on observed results, aiming to maximize efficiency and achieve desired outcomes with minimal resources. Current research focuses on developing robust algorithms, such as Bayesian optimization and reinforcement learning, often coupled with neural networks (including attentive neural processes and deep image priors) to handle complex, high-dimensional problems across diverse fields. These advancements are improving the efficiency of tasks ranging from traffic light management and enzyme design to material science and medical imaging, impacting both scientific discovery and real-world applications.