Abstract.
How can physicists learn the most physics from their limited data and resources? This challenge often comes down to fighting various aspects of the "curse of dimensionality". In this talk I will introduce several related computational challenges in high-energy phenomenology, and discuss a complementary set of approaches to tackle these, ranging from smarter utilisation of HPC resources to robotics-inspired machine learning.
(Unless this is a blackboard talk, the slides will be available here)