Join

Cyber Huntsville 

&

AI Huntsville

for our continuing Tech Tuesday Breakfast Series!

November 4, 2025 at 7:30AM

Hudson Alpha Atrium at – 601 Genome Way

Breakfast Provided

The convergence of AI and Cyber in Quantum

with

Dr. Sergei Gleyzer

Associate Professor

Dr. Sergei Gleyzer works on new physics phenomena at the Large Hadron Collider (LHC) of the CERN particle physics laboratory, located in Switzerland. He has been an integral part of the team that discovered the Higgs boson in 2012 and is now involved in the measurement of the properties of this new particle to pin down effects of any new physics beyond the Standard Model. Dr. Gleyzer’s research explores novel approaches to physics analysis, particle and event identification, detector reconstruction, simulation and particle physics triggering systems. Dr. Gleyzer works on the development of artificial intelligence techniques for new physics, including searches for rare decays of the Higgs boson and dark matter using the data collected by the Compact Muon Solenoid (CMS) experiment. In particular, his group has focused on using machine learning techniques to search for the extremely rare decays of the Higgs boson decaying into particles such as photons, electrons, and taus. In another line of research, his group is attempting to identify the nature of dark matter among the many collisions produced by the Large Hadron Collider (LHC). In a complimentary direction aimed at understanding the nature of dark matter, Dr. Gleyzer’s group has developed a novel machine learning approach to probe dark matter with strong gravitational lensing. Dr. Gleyzer also contributes to the development of the High-Granularity Calorimeter for CMS Endcap and the High Luminosity LHC project. Dr. Gleyzer is founder of the Inter-experimental LHC Machine Learning (IML) Working group, founding convener of the CMS Experiment’s Machine Learning Forum and the Machine Learning for Science (ML4SCI) Foundation.

Other research interests include pure machine learning and computer science theory of optimization, particularly by automatic feature extraction and breaking down the learning algorithms to their most basic pieces and finding ways to optimize them.