Please join us in the Electrical and Computer Engineering Department at the Technological Institute for a Meet-the-Faculty seminar with research associate Raman Khurana, PhD.

Student Introduction by Manuel Blanco Valentin from 3:45 pm - 3:55 pm.

Abstract: Fundamental physics is now a big-data problem. From the subatomic debris of proton collisions to the fragile coherence of quantum systems, the volume and complexity of modern experimental data demand new approaches to reconstruction, classification, and inference.

This talk explores the transformative role of Artificial Intelligence (AI) in high-energy particle physics and quantum information science. I will first trace how AI and data-driven methods have reshaped the search for the Higgs boson and dark matter at the CMS experiment, focusing on Higgs boson decays to pairs of bottom quarks. In this search, AI-driven b-tagging algorithms identify rare bottom-quark events amidst overwhelming and complex backgrounds, a task that traditional selection methods could not achieve with the same sensitivity. This has enabled the strongest experimental limits to date on the dark matter production cross section.

Moving from the scale of particle collisions to quantum systems, I will highlight how the similar AI driven approach is now being applied to address challenges ranging from fast quantum state measurement to real time photon polarization correction in the quantum networks. I will emphasize bringing the rigor of high-energy physics to AI models: going beyond prediction to quantify uncertainty and understand its impact on high-stakes decisions.

Bio: Dr. Raman Khurana is a Research Associate in the Department of Electrical and Computer Engineering at Northwestern University, where he works at the intersection of AI, complex data, and quantum computing. His current research advances quantum machine learning, with an emphasis on uncertainty quantification, rigorous benchmarking against classical baselines, and optimization. He also leads the development of machine learning methods for real-time photon polarization correction tackling a key challenge in building reliable quantum networks and communication systems.

Dr. Khurana built his AI expertise at the frontier of high-energy physics. As a member of the CMS Collaboration at CERN, he contributed to the discovery of Higgs boson. He then proposed and led three new dark matter searches involving Higgs bosons and b-quark jets. These analyses used machine learning to isolate rare signals in massive, complex datasets and set strongest exclusion limits on dark matter production cross-section. He served as convener of the CMS Jet and MET Data Quality Monitoring group. There he led a team of 15 researchers that ensured only high-quality data reached the experiment's physics searches.

Dr. Khurana serves as an editor of Frontiers of Physics and reviewer for leading physics and AI venues, including JHEP, NeurIPS, ICML, and ML4PS. He received his BSc and MSc in Physics and his PhD from the University of Calcutta, India, carrying out his doctoral research at CERN.