Desi Ivanova, a distinguished research fellow in machine learning at Oxford, dives into the fascinating world of Bayesian experimental design. She discusses how optimal experiment design is crucial for effective data gathering and uncertainty reduction. Desi sheds light on computational challenges and innovations like amortized Bayesian inference. The conversation also touches on real-world applications of these designs in healthcare and technology and the promising future advancements with AI that could reshape research methodologies.