Sequential Experiment

Sequential experimentation focuses on optimizing the design and execution of experiments where decisions about future trials depend on the results of previous ones. Current research emphasizes developing efficient algorithms, such as Bayesian optimization, reinforcement learning, and variations of Thompson sampling, often incorporating models like Hawkes processes or latent factor models to handle sequential data and improve decision-making under uncertainty. This field is crucial for accelerating scientific discovery and improving decision-making in diverse areas, including drug discovery, clinical trials, and materials science, by enabling more efficient and robust exploration of complex systems. The development of robust and computationally efficient methods for sequential inference, particularly in the face of noisy or incomplete data, remains a key focus.