M.S. in Physics
Aug 2024 – Expected May 2027
University of Puerto Rico – Mayagüez
Department of Physics
CV
PDFBasics
Name
Iliomar Rodríguez Ramos
Label
Physics Graduate Student · CMS Researcher · Machine Learning for Experimental Particle Physics
Location
Mayagüez, Puerto Rico, USA
Phone
Summary
Physics graduate student at the University of Puerto Rico at Mayagüez working at the
intersection of experimental particle physics, machine learning, and scientific software.
My current work focuses on CMS Run 3 Emerging Jets trigger-efficiency modeling,
machine-learning methods for CMS Data Quality Monitoring, and end-to-end simulation
studies with conditional generative models.
Education
B.S. in Theoretical Physics
Aug 2018 – May 2024
University of Puerto Rico – Mayagüez
Department of Physics · Minor: Astronomy and Astrophysics
Skills & Research Interests
Research Interests
CMS Experiment
Dark Matter Searches
Dark QCD
Emerging Jets
LLPs
Trigger Efficiency Modeling
Anomaly Detection
End-to-End Simulation
ML for Detector Quality Monitoring
Technical Skills
Python
C++
Bash
ROOT
PyTorch
TensorFlow
CMSSW
MadGraph
Pythia
FastJet
CMS DQM GUI
Docker
Singularity
Research Experience
Trigger Efficiency Modeling and Uncertainty Estimation
Jan 2025 – Present
CMS Collaboration · Conducted at UPRM
With guidance from Kevin Pedro, Associate Scientist at Fermilab
- Developed a neural network-based CMS trigger-efficiency model for Emerging Jet signatures in Run 3 analyses, producing smooth, differentiable efficiency estimates from generator- and reconstruction-level observables.
- Evaluated uncertainty estimates through training variations, model stability tests, and control-region versus signal-region comparisons.
- Built a reproducible Python analysis pipeline for data processing, training, validation, and visualization.
Machine Learning for Data Quality Monitoring (ML4DQM)
Jun 2025 – Present
CMS Collaboration · Conducted at UPRM
With guidance from Gabriele Benelli, LPC Researcher, and Richa Sharma, Postdoctoral Researcher
- Applied unsupervised ML methods, specifically non-negative matrix factorization, to CMS Data Quality Monitoring, focusing on anomaly detection in 2D tracking histograms.
- Studied model sensitivity and stability across runs and detector conditions, evaluating separation between nominal behavior and anomalous patterns in monitoring distributions.
- Contributing to CMS ML4DQM tooling, including development and integration efforts for DQMExplore and Reference Run Rank to support reference-run selection and data certification workflows.
End-to-End Simulation Studies with Conditional Generative Models
Sep 2025 – Present
Expand AI Research Group · Conducted at UPRM
With supervision of Dr. Sudhir Malik and Dr. Arghya Chattopadhyay, Postdoctoral Researcher
- Developed and evaluated end-to-end simulation pipelines using conditional flow-based generative models linking generator- and reconstruction-level representations.
- Explored extensions of the context vector with geometry- and physics-informed features to improve control, robustness, and interpretability of generated events.
Exploring Complex Structure of Ancient Stars
Jun 2023 – Jul 2023
REU at Michigan State University · East Lansing, MI
With supervision of Dr. Wolfgang Kerzendorf and Dr. Joshua Shields
- Calibrated and validated the open-source radiative-transfer code STARDIS for stellar atmosphere and spectral simulations using high-resolution solar reference data.
- Compared observational spectra from the University of Tabriz siderostat and spectrograph with synthetic STARDIS spectra to assess model fidelity and systematic effects.
- Contributed to a broader effort to model and interpret spectra of extremely distant, early-universe stars, including applications to objects such as Earendel.
A Statistical Analysis of Crab Pulsar Giant Pulse Rates
Jun 2023 – Jun 2024
Pulsar Science Collaboration · Conducted at UPRM
With supervision of Dr. Graham Doskoch
- Analyzed Crab pulsar giant pulse rates from radio observations, with emphasis on signal-to-noise-based event selection and statistical robustness.
- Quantified how S/N thresholds affect pulse-rate measurements, timing properties, and frequency-dependent behavior.
- Co-authored a refereed publication in The Astrophysical Journal reporting statistical properties of Crab giant pulses.
Publication
G. M. Doskoch, A. Basuroski, K. Halley, A. Sookram,
I. Rodríguez Ramos, V. Nahata, Z. Rahman, M. Zhang,
A. Uhlmann, A. Lynch et al.,
“A Statistical Analysis of Crab Pulsar Giant Pulse Rates,”
The Astrophysical Journal, 2024.
DOI:
10.3847/1538-4357/ad6304
.
Workshops & Schools
Python for Analysis Workshop
IRIS-HEP Software Basics Training
CMS Data Analysis School
QuarkNet Workshop
Plasma Physics Workshop
Teaching & Outreach
Conducted a Python training workshop for faculty
Led introductory Python training workshops for high school students
Taught a Bash shell tutorial as part of HSF training
Delivered a Bash shell tutorial for PURSUE interns
Physics Laboratory Instructor
Presentations
Trigger Efficiency Modeling for Emerging Jets Using Neural Networks
Exploring Complex Structures of Ancient Stars
Exploring Complex Structures of Ancient Stars
Exploring Complex Structures of Ancient Stars
Exploring Complex Structures of Ancient Stars