The Sargent Centre for Process Systems Engineering at Imperial College London is hosting its third edition summer school, on the topic of Data-driven optimization: Bayesian optimization. The school will consist of preliminary tutorials on Gaussian processes and Bayesian optimization, attendees will gain a foundation in the underlying principles, and learn probabilistic modeling and sequential decision-making. With hands-on exercises, students will develop their understanding of how Gaussian processes serve as powerful tools for modeling uncertainty and guiding decision-making processes.

As the program develops, participants will explore Bayesian optimization from different experts, going into diverse applications across high-dimensional spaces, molecular discovery, real-time optimization, and safety-critical systems in science and engineering. From navigating the complexities of multi-fidelity Bayesian optimization to harnessing the potential of batch Bayesian optimization for high-throughput experimentation, students will discover how Bayesian techniques can be used both in industry and academia.