Philip Harris seeks to discover dark matter and understand the fundamental properties of the Higgs boson. He searches for dark matter in many different forms, from small experiments using a proton beam dump to CMS detector on the Large Hadron Collider. He has performed some of the most precise measurements of the electroweak force in his work. His work complements more conventional dark matter satellite and direct detection experiments providing a new angle. Philip has extended this work towards measurements of the Higgs boson properties and even more exotic signatures, which rely on Artificial Intelligence(AI). His work has heralded a new strategy of Higgs boson measurements that relies on AI to bring a deeper understanding of Higgs interactions.
Philip’s research exploits new techniques in deep learning to search for the unknown as well as new approaches to resolve the structure of quark and gluon decays, known as jet substructure. As one of the founders of the Fast Machine Learning organization, Philip is leading a considerable effort to build real-time deep learning systems using new types of processor technology, including FPGAs, GPUs, and ASICs. This ranges from deep learning on petabit/s data at the LHC to multi-messenger astronomy with gravitational wave detection. Philip maintains an interest in jet substructure measurements in the quark-gluon medium of heavy ion collisions, along with an interest in cutting-edge machine learning techniques.