About
My name is Dimitrios (but most people call me Dimitris) Ntounis [/di-MEE-tris DOO-nis/] and I come from Athens, Greece. I am currently a PhD Candidate in the Physics Department at Stanford University. I am a researcher at SLAC National Accelerator Laboratory working with the SLAC ATLAS group. I’m also pursuing a PhD Minor in Computer Science at Stanford.
You can find a few key points about me below or read my Projects page or CV for more details.
Education
- PhD in Physics, Stanford University, in progress
- PhD Minor in Computer Science, Stanford University, in progress
- MSc in Physics, Stanford University, 2024
- BSc in Physics, National and Kapodistrian University of Athens, 2021
Research Interests
- Higgs Boson Physics with the ATLAS experiment at the CERN LHC
- Future electron-positron colliders
- Machine Learning for Science
Research
My research lies at the intersection of experimental particle physics, accelerator science, and machine learning. I am driven by a fundamental question: What are the building blocks of the Universe, and how do they interact?
Higgs Boson Physics at the LHC: I analyze petabyte-scale datasets from the ATLAS detector at CERN’s Large Hadron Collider (LHC) to probe the properties of the Higgs boson—the particle intimately tied to the mechanism that gives fundamental particles their mass. My work focuses on precision measurements of Higgs production in association with vector bosons, using advanced statistical techniques to extract signals from challenging backgrounds.
Next-Generation Colliders: Looking beyond the LHC, I contribute to the development of future electron-positron colliders designed to achieve unprecedented precision in Higgs, electroweak, and top quark measurements. My contributions include comprehensive studies of the beam dynamics, luminosity optimization, and beam-induced background characterization for the Cool Copper Collider (C³), a newly proposed US-led linear collider concept. I also benchmark jet flavor tagging algorithms across different detector configurations to inform optimal detector design.
AI/ML for Discovery: I leverage state-of-the-art machine learning techniques to extract maximum physics insight from available data. This includes developing novel algorithms for particle identification that enhance our sensitivity to new physics signatures.
In the News
Research Network
Interactive visualization of my research collaborations from INSPIRE-HEP. Excludes large collaboration papers (ATLAS/CMS) to show individual co-authors.
Showing individual co-authors only. Papers with >20 authors (ATLAS/CMS) are excluded.
From the Blog
Loading recent posts...
Interested in collaborating or have questions? Get in touch or schedule a meeting.