Invited talk: Pushing the limits of real-time ML: Nanosecond inference for Physics Discovery at the LHC

I am a particle physicst currently working as a senior research fellow at ETH Zurich. Here I work on utilizing Machine Learning to improve the way we collect and analyze data at the Large Hadron Collider. I have a PhD in physics from the University of Zurich, where I worked on searches for new heavy particles decaying into dibosons and jet substructure techniques. Currently, I specialize in real-time Machine Learning to help us process enormous data rates (63 Tb/s) at LHC, within a latency of 12 microseconds. I also work on utilizing ML-based anomaly detection as a way of discovering New Physics as data outliers. When I am not writing code or trail running, I supervise students on various projects as well as give schools and seminars on ML in particle physics.