Dr. Gleyzer is an assistant professor in the Department of Physics and Astronomy. His research involves the development of artificial intelligence techniques for new physics searches, including rare decays of the Higgs boson and dark matter using the data collected by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). Dr. Gleyzer served as the founding CMS coordinator of the Inter-experimental LHC Machine Learning Working Group (IML) and as a founding convener of the CMS Machine Learning Forum. Dr. Gleyzer’s research explores novel approaches to physics analysis, particle and event identification, detector reconstruction, simulation and particle physics triggering systems. He is additionally a member of the LSST Strong Lensing Science Collaboration. He frequently teaches physics and machine learning and is interested in interdisciplinary research involving machine learning. He is additionally the founder of Machine Learning for Science (ML4SCI) foundation and organizer of the Machine Learning for Science hackathons. Dr. Gleyzer is currently supported by the Department of Energy, National Science Foundation and industry grants. Dr. Gleyzer’s website: http://sergeigleyzer.com
- End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC. M. Andrews, M. Paulini, S. Gleyzer, B. Poczos. Computing Software for Big Science. 4. 10.1007/s41781-020-00038-8, 2020
- End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data. M. Andrews, J. Alison, S. An, P. Bryant, B. Burkle, S. Gleyzer, M. Narain, M. Paulini, B. Poczos, E. Usai. Nuclear Instruments and Methods Physics Research A 977:164304, 10.1016/j.nima.2020.164304, 2020.
- Deep Learning the Morphology of Dark Matter Substructure. S. Alexander, S. Gleyzer, E. McDonough, M. Toomey, E. Usai. The Astrophysical Journal. 893: 15, 10.3847/1538-4357/ab7925, 2020
- Machine Learning in High-Energy Physics Community White Paper, K. Albertsson, et al. J Phys.: Conf Ser. 1085: 022008, 2018.
- Observation of Higgs boson decay to bottom quarks, CMS Collaboration, Phys Rev Lett. 121: 121801. 10.1103/PhysRevLett.121.121801, 2018
Machine learning in high-energy physics analysis, reconstruction, simulation and real-time applications, physics-inspired machine learning, Quantum AI, machine learning for science