How can we intelligently acquire information for decision making, when facing a vast space of possible data? In this talk, I will focus on Bayesian experimental design problems that arise naturally in the physical sciences, and present how we can develop principled approaches that actively extract information, identify the most relevant data for the tasks, and make effective decisions under uncertainty. I will talk about a few practical challenges in real-world decision-making systems such as multi-fidelity feedback signals and complex constraints. I will elaborate on how to address these practical concerns on a variety of application domains, ranging from nanophotonics, protein engineering to cosmic discovery.
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