Sergei V. Gleyzer, Ph. D.
Office: Gallalee Hall 320B
Office Fermilab: WH11E
Office CERN: 32-R-C17
Telephone: 1 (205) 348-7869
Email: sgleyzer@ua.edu
Email: sergei@cern.ch
Address:
University of Alabama
Department of Physics and Astronomy
Tuscaloosa, AL 35401
United States
EDUCATION
Ph.D. Florida State University, High-Energy Physics, Thesis: “Search for the 2011
Dark Matter Signature in Lepton Jet Final State at √s = 7 TeV”,
Graduate advisor: Vasken Hagopian
M.S. Florida State University, High-Energy Physics, advisor: Vasken Hagopian 2006
B.S. Carnegie Mellon University, Physics 2004
ACADEMIC POSITIONS
Co-Director Alabama Center for the Advancement of Artificial Intelligence, USA 2024 –
Associate Professor Department of Physics and Astronomy, University of Alabama, USA 2023 –
Assistant Professor Department of Physics and Astronomy, University of Alabama, USA 2019 – 2023
Dist. Research Fellow Department of Physics, University of Florida, USA 2015 – 2019
Research Fellow Deutches Elektronen-Syncrotron (DESY), Germany 2012 – 2015
Research Assistant Department of Physics, Florida State University, USA 2006 – 2011
SELECTED GRANTS and AWARDS
APS: Fellow 2024
UA: Office or Research and Economic Development Senior Fellow 2024 – 2026
Fermilab: LPC Senior Distinguished Researcher Award (PI) 2025
Fermilab: 2 x LPC Distinguished Researcher Award for Postdoc (PI) 2024 – 2026
US. CMS: Activities related to the HL-LHC CMS Detector Upgrade Project (PI) 2024 – 2026
DOE: SBIR Office of Science Award (PI) 2024 – 2025
DOE: NERSC AY 2024 DOE Mission Science Allocation Award (PI) 2024 – 2025
DOE: Artificial Intelligence for High-Energy Physics (PI) 2023 – 2026
DOE: Research in Elementary Particle Physics: Energy Frontier (Co-PI) 2023 – 2026
DOE: NERSC AY 2023 Director Reserve Allocation Award (PI) 2023 – 2024
NSF: NRT ACCEPT (Co-I, Machine Learning Lead) 2023 – 2028
UA: College Academy of Research, Scholarship and Creative Activity Award (PI) 2023 – 2024
UA: Distinguished Teaching Fellowship 2022 – 2025
DOE: Research in Elementary Particle Physics: Energy Frontier (Co-PI) 2020 – 2023
US. CMS: 3 x Software&Computing R&D for HL-LHC Postdoc Co-Fund Award (PI) 2020 – 2023
NSF: MPS-HIGH: Decoding Dark Matter through Gravitational Lensing (PI) 2022 – 2023
NSF: Slingshot: Decoding Dark Matter through Gravitational Lensing (PI) 2021 – 2024
UA: College Academy of Research, Scholarship and Creative Activity Award (PI) 2021 – 2022
UA: Office for Research and Economic Development SGP Award (PI) 2020 – 2021
UA: CyberSeed Award: Deep Learning and Dark Matter (PI) 2020 – 2021
UA: Learning in Action Fellowship 2020 – 2021
NSF: Circumgalactic Dictionary: An Interpretation Guide (Co-PI) 2020 – 2023
Fermilab: 2 x LHC Physics Center Distinguished Researcher Fellowship 2018 – 2020
EU H2020: INSIGHTS Grant Award in Machine Learning 2018 – 2021
NASA: Grant Award for Machine Learning for Planetary Science 2016 – 2019
DESY: Research Fellowship 2012 – 2014
Florida State: Hagopian Family Endowment Award in High Energy Physics 2009
COMMUNITY and RESEARCH ACCOMPLISHMENTS
Member of Compact Muon Solenoid (CMS) Collaboration 2006 – present
Member of Rubin Observatory LSST Strong Lensing Science Collaboration 2020 – present
Convener of CMS Collaboration Machine Learning Forum 2017 – 2020
Coordinator of the LHC Machine-Learning Working Group 2016 – 2018
Founder, convener of Inter-Experimental Machine Learning Working Group 2016 – present
Founder, administrator of Machine Learning for Science (ML4SCI) Organization 2018 – present
Founder, administrator of HumanAI Foundation 2023 – present
Inaugural Co-Director, Alabama Center for the Advancement of Artificial Intelligence 2024 – present
Organizer, Chair and Coordinator QCHS2024, QCHS2022, AAAS2021, vQCHS2021, ICNFP 2019, 2016 – present
LHCP 2019, SUSY 2019, CPAD 2018, MLJETS 2018, QCHS 2018, CHEP 2018
RECENT INVITED PLENARY TALKS AND LECTURES
11th International School of Deep Learning Jul. 2024
invited lectures, Porto, Portugal
2024 Hagopian Lecture Mar. 2024
invited prize lecture, Florida State University, Tallahassee, USA
Miami 2023 Dec. 2023
invited plenary talk on “Dark Matter Searches with Machine Learning”, Ft. Lauderdale, United States
KIAS AI in High-Energy Physics Summer School Jul. 2023
“Quantum Machine Learning”, invited lectures, Seoul, South Korea
Workshop on Strong Gravitational Lensing in the Era of Big Data Jun. 2023
“Machine Learning-based Analysis and Inference”, Otranto, Italy
9th International School of Deep Learning Apr. 2023
“Machine Learning Fundamentals and Their Applications to Large Scientific Data”, lectures, Bari, Italy
Workshop on Machine Learning for Cosmic Ray Showers Feb. 2022
“Machine Learning for Particle Physics”, Newark, DE, USA
NSF AI Planning Institute for Data Discovery in Physics Dec. 2021
“AI in Physics Seminar”, Pittsburgh, USA
ENFPC 2021 Sep. 2021
“Modern Deep Learning in High-Energy Physics”, invited plenary talk, Brazil
AI4EIC 2021 Sep. 2021
“Machine Learning for Physics Analysis”, invited talk, USA
Yale Center for Astronomy and Astrophysics May 2021
“Deep Learning for High-Energy Physics and Strong Gravitational Lensing Cosmology”, invited seminar, USA
University of Puerto Rico Mayagüez Oct. 2020
“Deep Learning for High-Energy Physics and Strong Gravitational Lensing Cosmology”, invited colloquium, USA
ENFPC 2020 (postponed to 2021) Sep. 2020
invited plenary talk on “Machine Learning Advances in Particle Physics”, Natal, Brazil
APS 2020 April Meeting Apr. 2020
invited plenary talk on “Machine Learning in High-Energy Physics”, Washington DC, United States
Miami 2019 Dec. 2019
invited plenary talk on “Machine Learning at the LHC”, Ft. Lauderdale, United States
Global Innovation Forum: Transforming Intelligence Oct. 2019
invited keynote talk on “Science and Artificial Intelligence”, Yerevan, Armenia
Interpreting LHC Run 2 Data and Beyond Workshop June 2019
invited plenary talk on “Machine Learning at the Large Hadron Collider”, ICTP Trieste, Italy
Inter-experimental LHC Machine Learning Workshop Apr. 2019
invited keynote talk on “Machine Learning in High-Energy Physics: Today and Tomorrow”, CERN
University of Alabama Physics Department Mar. 2019
“Opportunities for New Physics with Modern Deep Learning in CMS”, colloquium, Tuscaloosa, USA
Fermilab Wine and Cheese Seminar Nov. 2018
“Deep Learning for the Future of High-Energy Physics”, Chicago, USA
World Vaccine and Immunization Congress West Coast 2018 Nov. 2018
invited plenary panelist on “Leveraging Power of AI & Machine Learning for
Accelerated Vaccine Development”, San Diego, USA
Heavy Particles and Flavor Physics (LISHEP2018) Sep. 2018
invited plenary, Salvador Bahia, Brazil
Machine Learning Conference in Science and Engineering May 2018
“Machine Learning at the Large Hadron Collider”, invited plenary, Pittsburgh, USA
CEA-Saclay Nov. 2017
“Machine Learning in Particle Physics”, invited seminar, Paris, France
XVIII International Workshop on Advanced Computing and Analysis Techniques Aug. 2017
“Machine Learning Advancements in Particle Physics”, invited plenary, Seattle, USA
International Workshop on Big Data Tools for Physics and Astronomy June 2017
“Machine Learning at the LHC”, invited plenary, Amsterdam, Netherlands
Machine Learning Challenges in Complex Multiscale Physical Systems Jan. 2017
“Machine Learning in High-Energy Physics”, invited plenary, Munich, Germany
SELECTED PUBLICATIONS
- M. Knipfer, S. Meier, J. Heimerl, P. Hommelhoff and S. Gleyzer,”Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay-Line Detectors“, Mach. Learn.: Sci. Technol. 5 (2024) 025019
- M. Comajoan Cara et al. (including S. Gleyzer), “Quantum Vision Transformers for Quark Gluon Classification”, Axioms 13 (2024) 323
- Z. Dong et al. (including S. Gleyzer), “ℤ2×ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks”, Axioms 13 (2024) 188
- R. Forestano et al. (including S. Gleyzer), “A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks”, Axioms 13 (2024) 160
- E. Unlu et al. (including S. Gleyzer), “Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics”, Axioms 13 (2024) 187
- P. Reddy, M. Toomey, H. Parul and S. Gleyzer, “DiffLense: A Conditional Diffusion Model for Super-Resolution of Gravitational Lensing Data” (2024)
- CMS Collaboration (including S. Gleyzer), “Search for Exotic Higgs Boson decays H to AA to 4 gamma with events containing two merged diphotons in proton-proton collisions at sqrt(s) = 13 TeV“, Physical Review Letters 131 (2023) 101801
- A. Alnukaydan, S. Gleyzer, H. Prosper, E. Reinhardt, F. Charton and N. Anand, “Symbolic Machine Learning for High Energy Physics Calculations“, NeurIPS2023 Machine Learning and the Physical Sciences (2023)
- G. Cheeramvelil, M. Toomey and S. Gleyzer, “Equivariant Neural Networks for Signatures of Dark Matter Morphology in Strong Lensing Data“, NeurIPS2023 Machine Learning and the Physical Sciences (2023)
- Y. Deshmukh, K. Sachdev, M. Toomey and S. Gleyzer, “Learning Dark Matter Representation from Strong Lensing Images through Self-Supervision“, NeurIPS2023 Machine Learning and the Physical Sciences (2023)
- J. Velôso de Souza, M. Toomey and S. Gleyzer, “Lensformer: A Physics-Informed Vision Transformer for Gravitational Lensing“, NeurIPS2023 Machine Learning and the Physical Sciences (2023)
- J. Terry, S. Gleyzer, “Locating Hidden Exoplanets with Machine Learning“, NeurIPS2023 Machine Learning and the Physical Sciences (2023)
- J. Terry, C. Hall, S. Abreu and S. Gleyzer, “Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning“, Astrophysical Journal 947 (2023) 60
- CMS Collaboration (including S. Gleyzer), “Reconstruction of Decays of Merged Photons using End-to-end Deep Learning with Domain Continuation in the CMS Detector“, Physical Review D 108 (2023) 052002
- S. Alexander, S. Gleyzer, P. Reddy, M. Tidball and M. Toomey, “Domain Adaptation for Simulation-Based Dark Matter Searches using Strong Gravitational Lensing“, Astrophysics Journal 954 (2023) 28
- A. Alnukaydan, S. Gleyzer and H. Prosper, “SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning“, Mach. Learn.: Sci. Technol. 4 (2023) 015007
- R. Chudasama et al. (including S. Gleyzer), “End-to-end Deep Learning Inference with CMSSW using ONNX”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics (2023)
- J. Terry, C. Hall, S. Abreu and S. Gleyzer, “Locating Hidden Exoplanets in ALMA Data using Machine Learning“, Astrophysical Journal 941 (2022) 192
- S. Alexander, S. Gleyzer, P. Reddy, M. Tidball and M. Toomey, “Domain Adaptation for Dark Matter Searches using Strong Gravitational Lensing“, NeurIPS2022 Machine Learning and the Physical Sciences (2022)
- M. Knipfer, S. Meier, J. Heimerl, P. Hommelhoff and S. Gleyzer,”Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay-Line Detectors”, NeurIPS2022 Machine Learning and the Physical Sciences (2022)
- M. Andrews et al. (including S. Gleyzer), “End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data“, Physical Review D 105 (2022) 052008
- A. Hariri, S. Gleyzer, D. Dyachkova, “Graph Generative Models for Fast Detector Simulations in High Energy Physics”, arXiv:2104.01725 (2021)
- A. Sirunyan et al. (including S. Gleyzer), “Evidence of Higgs boson decays to a pair of muons”, Journal of High Energy Physics 01 (2021) 148
- M. Andrews et al. (including S. Gleyzer), “End-to-end Jet Classification of Boosted Top Quarks”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web Conf. 251 (2021) 04030
- A. Hariri, S. Gleyzer, D. Dyachkova, “Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web Conf. 251 (2021) 04030
- M. Andrews et al. (including S. Gleyzer), “Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web Conf. 251, 04030
- S. Alexander, S. Gleyzer, H. Parul, P. Reddy, M. Toomey, E. Usai and R. von Klar, “Decoding Dark Matter Substructure Without Supervision“, arXiv:2008.12731 (2020)
- S. Alexander, S. Gleyzer, E. McDonough, M. Toomey and E. Usai, “Deep Learning the Morphology of Dark Matter Substructure“, Astrophysical Journal 893 (2020)
- M. Andrews et al. (including S. Gleyzer), “End-to-End Identification of Quarks and Gluons with the CMS Open Data”, Nuclear Instruments and Methods A 977 (2020) 164304
- S. Gleyzer et al., “End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC“, Computing and Software for Big Science 4 (2020) 6
- M. Andrews, M. Paulini, S. Gleyzer and B. Poczos, “End-to-End Deep Learning for Particle and Event Classification”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web of Conferences 214, 06031 (2019)
- D. Bourilkov et al. (including S. Gleyzer), “Machine Learning Techniques in the Search for the Higgs Boson in the di-muon Final State”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web of Conferences 214, 06002 (2019)
- Z. Ahmed et al. (including S. Gleyzer), “New Technologies for Discovery“, report of the 2018 DPF Coordinating Panel for Advanced Detectors, July 2019
- J. Albrecht et al. (including S. Gleyzer), “A Roadmap for High-Energy Physics Software and Computing R&D for the 2020s“, arXiv:1712.06982, Comput. Softw. Big Sci., 2019, 3: 7
- A. Sirunyan et al. (including S. Gleyzer), “Search for the Higgs boson production in the di-muon final state with pp collisions at √s = 13 TeV”, Phys. Rev. Lett., 021801, 2018
- A. Sirunyan et al. (including S. Gleyzer), “Observation of Higgs Boson Decay to Bottom Quarks“, Phys. Rev. Lett. 121, 121801, 2018
- S. Gleyzer et al., “The Rise of Deep Learning”, CERN Courier, 2018
- A. Sirunyan et al. (including S. Gleyzer), “Search for the Higgs boson production in the di-muon final state with pp collisions at √s = 13 TeV”, 2018
- K. Albertsoon et al. (including S. Gleyzer as main editor), “Machine Learning in High Energy Physics: Community White Paper”, arXiv:1807.02876, 2018
- S. Gleyzer et al., “End-To-End Deep Learning for Event Classification”, in Proceedings of XVIII International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 2018
- D. Berzano et al. (including S. Gleyzer),“High Energy Physics Software Foundation Community White Paper: Training, Staffing and Careers“, arXiv:1807.02875, 2018
- S. Gleyzer et al., “Machine Learning Developments n ROOT”, in Proceedings of International Conference on Computing in High Energy and Nuclear Physics, 2017
- D. Acosta et al. (including S. Gleyzer), “Boosted Decision Trees in the CMS Level-1 Endcap Muon Trigger ”, in Proceedings of Topical Workshop in Electronics for Particle Physics, October 2017
- S. Gleyzer et al., “Machine-Learning Development in High-Energy Physics”, in Proceedings of the XII Quark Confinement and the Hadron Spectrum Conference, 2017
- S. Gleyzer et al., “Accelerating High-Energy Physics Exploration with Deep Learning”, in Proceedings of the Practice and Experience in Advanced Research Computing, 2017
- S. Gleyzer et al., “Falcon: Towards an Ultra Fast Non-Parametric Detector Simulator”, ”, in Proceedings of the New Physics Working Group of the 2015 Les Houches Workshop, Physics at TeV Colliders, arXiv:1605.02684, 2016
- S. Chatrchyan et al. (including S. Gleyzer), “Measurement of Prompt Double J/ψ production, Journal of High Energy Physics”, JHEP 09 (2014) 094
- S. Chatrchyan et al. (including S. Gleyzer), “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”, Phys. Lett. B 716 (2012) 30
- S. Gleyzer and H. Prosper, “PARADIGM: Decision-Making Framework for Variable Selection and Reduction in High Energy Physics”, in Proceedings of the XII International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 2009
- S. Abdullin et al. (including S. Gleyzer), “The CMS barrel calorimeter response to particle beams from 2 to 350 GeV/c”, Eur. Phys. J. C 60 (2009) 359
- S. Gleyzer et al., “The h0 to A0A0 to bbτ+τ– Signal in Vector Boson Fusion Production at the LHC”, Proceedings of the Higgs Boson Working Group of the 2007 Les Houches Workshop, Physics at TeV Colliders, arXiv:0803.1154, 2007
- Full List of Publications
SERVICE ACTIVITIES
American Journal of Physics Resource Letters Editorial Board 2022 – 2025
SNOWMASS Planning Process: Energy Frontier and Community Engagement Frontier Liason 2020 – present
Organizer of Machine Learning For Science (ML4SCI) Google Summer of Code Program 2021 – present
Organizer of HumanAI Google Summer of Code Program 2021 – present
PDG Consultant: Machine Learning Chapter 2021
NSF CDS&E Proposal Review Panel Member 2020
Organizer Machine Learning for Science Hackathons 2018 – present
Editor and organizer HEP Community White Paper on Machine Learning 2019
NSF Cyberinfrastructure for Sustained Scientific Innovation Review Panel Member 2018
Reviewer PRL, Nuclear Instruments and Methods, Computing and Software for Big Science, 2017 – present
IEEE Letters of Computer Society, Journal for High-Energy Physics and others
Founder of CERN-HSF Google Summer of Code Program 2017
CERN Knowledge Transfer (KT) Consultant on Machine Learning 2016 – present
Advisory Board Member of Machine Learning in HEP Summer School 2016 – present
Creator of Project CODER 2016 – present
Co-founder of Open Data Working Group CERN High School Teacher Program 2016 – present
CMS Data Quality Monitoring Supervisor (FNAL, DESY, CERN) 2009 – 2010
TEACHING
11th International School of Deep Learning (DeepLearn2024), invited lectures July 2024
UA PH551, PH451 Machine Learning Spring 2024
UA PH561 Nuclear and Particle Physics Fall 2023
KIAS AI in High-Energy Physics Summer School, invited lectures July 2023
9th International School of Deep Learning (DeepLearn2023), invited lectures April 2023
UA PH551, PH451 Machine Learning Spring 2023
IceCube EPSCOR Initiative Summer School, invited lectures June 2022
UA PH582, PH482 Machine Learning Spring 2022
LHC Physics Center (LPC) Machine Learning Course Spring 2022
4th International School of Deep Learning (DeepLearn2021), invited lectures July 2021
UA PH582, PH482 Machine Learning Spring 2021
UA PH101 General Physics Fall 2020
UA PH101 General Physics (Studio Format) Spring 2020
3rd International Summer School of Deep Learning (DeepLearn2019), invited lectures 2019
INFN School of Statistics 2019, invited machine learning lectures 2019
Workshop on Statistics and Machine Learning, CERN, machine learning lectures 2018
2nd International Summer School of Deep Learning (DeepLearn2018), invited lectures 2018
Workshop on Machine Learning, UNAM, invited lectures 2018
International Summer School of Particle Physics, invited lectures 2017
European Scientific Institute, invited lectures and tutorials in machine learning 2016 – 2017
National Academy of Science of Ukraine, invited lectures in machine learning 2017
CERN Open Lab, invited lectures and tutorials in machine learning 2016 – 2018
CERN High School Teacher Program, Open Data Working Group, 5 training sessions 2016
CERN EPLANET Mini-Course, Brazil, invited lectures and tutorials in machine learning 2015
Data Science at LHC Workshop, 2 invited lectures and tutorials in machine learning 2015
DESY Statistics School, 3 lectures and tutorials in machine learning 2014
Southeastern CMS Physics Workshop, lecture and tutorial in machine learning 2007
Carnegie Mellon University, Florida State University Physics/Astronomy Laboratory 2003 – 2005
PUBLIC ENGAGEMENT
US LHC Users Association , High-Energy Physics Community Trip to US Congress, Washington DC 2023
UA K-12 Teacher Coding Workshop, Tuscaloosa, AL 2023
US LHC Users Association , High-Energy Physics Community Trip to US Congress, Washington DC 2022
Machine Learning For Science (ML4SCI) Hackathon, hybrid, organizer 2022
US LHC Users Association , High-Energy Physics Community Trip to US Congress, Washington DC 2021
Machine Learning For Science (ML4SCI) Hackathon, virtual, organizer 2020
US LHC Users Association , High-Energy Physics Community Trip to US Congress, Washington DC 2020
Machine Learning Hackathons, UA, INFN, CERN, Brown, University of Puerto Rico, organizer 2017 – present
CS4RI High School Student Outreach 2018 – present
Workshop on Outreach Training at CERN, organizer 2018
SCIFOO Camp, invited participant, GoogleX, Palo Alto, California 2018
Fermilab UEC High-Energy Physics Community Trip to US Congress, Washington DC 2018
US LHC Users Association , High-Energy Physics Community Trip to US Congress, Washington DC 2017
CODER High School Teacher Workshop, CMS Open Data Analysis with Jupyter, UF 2017
CODER High School Teacher Workshop, CMS Open Data Analysis with Jupyter, UF 2016
CODER High School Teacher Workshop, CMS Open Data Analysis with Jupyter, UCF 2016
Belmont Hill High School, Outreach Talk on the Large Hadron Collider and Higgs Boson 2014
FameLab Germany 2013, Outreach Talk on the Discovery of the Higgs Boson 2013