Biography

I am a Ph.D. candidate in Nuclear Physics at the University of Kansas. My research focuses on forward physics with the CMS Zero Degree Calorimeter (ZDC) at the CERN Large Hadron Collider. Over the past year, I have been working on calibrating the hadronic section of the ZDC using O–O collision data and is extending this framework to the 2025 P–O dataset to extract neutron spectra in asymmetric collisions. My work combines detector calibration, ROOT/CMSSW workflows, and statistical modeling, with growing use of data-science and machine-learning techniques for signal extraction and uncertainty quantification. My long-term goal is to advance AI-enabled instrumentation and analysis methods that improve the precision and reach of nuclear-physics measurements.

Academic Status

PhD Student - 4th

Research Area/Department

Data Science; Physics

Major/Specialty

Experimental high energy nuclear physics

Degrees Earned or in Progress

MSc Physics, University of Kansas, May 2024 PhD Physics, University of Kansas, January 2022-Present,

Academic Preparation

computational Physics advanced data science computer vision Machine learning

Research/Publications

i currently work with the CMS collaboration at CERN. i currently work on understanding the detector capability of the zero-degree calorimeter.

Research/Academic Interests

My research interests lie at the intersection of data science, machine learning, detector technology, artificial intelligence, and advanced instrumentation, with a strong focus on their applications in nuclear physics. I am particularly interested in exploring how emerging computational methods, especially artificial intelligence, can be leveraged to enhance experimental techniques, improve detector performance, and deepen our understanding of nuclear matter. Looking ahead, I am motivated to investigate how advances in nuclear physics, coupled with AI-driven approaches, will shape both fundamental research and broader technological developments in the years to come.

Computational and Data Science Areas

Informatics, Analytics and Information Science; Particle and High-Energy Physics; Visualization and Human-Computer Systems

Motivation

I am a Ph.D. candidate in Nuclear Physics at the University of Kansas, focusing my research on forward physics measurements utilizing the CMS Zero Degree Calorimeter at the CERN Large Hadron Collider. This study integrates detector calibration, extensive data processing, and physics interpretation to investigate neutron production and nuclear halting in heavy-ion collisions. These endeavors have highlighted the essential role of improved computational techniques in the future of nuclear physics. My motivation to engage in the Sustainable Research Pathways - NAIRR Program arises from a desire to enhance my proficiency in data science, machine learning, and artificial intelligence, and to utilize these tools to address difficulties in nuclear instrumentation and detector technologies. I perceive AI-driven methodologies as revolutionary for the analysis of extensive datasets produced in high-energy physics experiments and for enhancing detector efficacy. Engaging with NAIRR resources and collaborating across disciplines would enable me to expand my work beyond physics, contributing to collective experimental and computational frameworks that benefit the wider scientific community. My objective is to establish a research career that integrates nuclear science with AI-driven data science, and I contend that SRP–NAIRR offers the optimal platform to gain new competencies, forge enduring collaborations, and make significant contributions to the advancement of national research infrastructure.

Lightning Talk Title

Advancing Heavy-Ion Physics with AI-Driven ZDC Analysis at CMS

Keywords (Maximum 20 words)

Data Science: AI: Machine Learning: Nuclear Science: High performance computing