Philip Harris PhD '11

Assistant Professor of Physics
Building Real-Time Deep learning Systems to search for Dark Matter and understand the Higgs boson
Affiliated Center(s): Laboratory for Nuclear Science
Assistant: Alisa Cabral

Research Interests

Philip Harris seeks to discover dark matter and understand fundamental properties of the Higgs boson. To search for Dark Matter, he explores a suite of collider searches which probe dark matter at the highest energies. In his work, he has performed some of the most precise measurements of the production of heavy light-like particles, the electroweak bosons. His work complements more conventional dark matter satellite and direct detection experiments providing a new angle of constraints. Recently, Philip has extended this work towards measurements of Higgs boson properties and even more exotic signatures. His work has opened up a new kinematic regime of Higgs bosons that allows for new types of measurements of the Higgs Boson.

Philip’s research exploits new techniques in deep learning to search for the unknown as well as new techniques to resolve the structure of quark and gluon decays, known as jet substructure. Philip is also leading a large effort to build realtime deep learning systems using new types of processor technology. This ranges from deep learning on petabit/s data at the LHC to gravitational wave detection. Philip maintains an interest in jet substructure measurements in the quark gluon medium of heavy ion collisions, along an interest in machine learning techniques.

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Biographical Sketch

Philip Harris joined the MIT faculty in 2017. Born in Sao Paulo, he received his B.S in Physics from Caltech in 2005, and his Ph.D from MIT in 2011 on research performed at CERN with the CMS experiment. From 2011-2013, Philip was a CERN fellow working on the Higgs discovery. From 2014-2017, he was a CERN staff scientist working on dark matter searches at the CMS experiment.

Awards & Honors

  • 2021 // DOE Office of Science Early Career Research Program Award
  • 2013 // Aspen Block Award