About me

Recent PhD graduate in experimental particle physics from UCLouvain and CERN (CMS).

Projects and portfolio

  • Transformer Classifier: a PyTorch transformer-encoder-based model for signal/background classification in HH → bbWW. GitHub
  • XGBoost Classifier: an XGBoost workflow for HH → bbWW event classification. GitHub
  • DY background estimation with adversary Variational Autoencoder (VAE): a prototype for data-driven background estimation using a VAE to learn latent representations of control region data. GitHub
  • Check my github repositories for more: https://github.com/Oguz-Guzel?tab=repositories

Theses

PhD: From Collisions to Predictions: Search for Non-Resonant Higgs Boson Pair Production in the Fully Leptonic bbWW Final State using CMS Run 3 Data, https://repository.cern/records/6yra5-d9w35.

MSc: Prospects of Nonresonant Higgs Boson Pair Production Measurement in the WWgg Channel at the HL-LHC with the Phase-II CMS Detector (Snowmass analysis), https://repository.cern/records/gtq7e-y1j18.

Talks and presentations

Selected presentations that bridge technical model work and data-driven physics analysis:

  • “EFT interpretations in the Higgs sector at the CMS experiment” — DIS2024, Grenoble, FRANCE. Details
  • “Higgs self-coupling measurements at the CMS experiment” — PASCOS 2024, Quy Nhon, VIETNAM. Program

Teaching and mentoring

  • Mentored junior researchers on ML model development, data analysis techniques, and reproducible research practices in the context of particle physics.
  • Physics lab tutor, electromagnetism course at Istanbul Technical University. Supported undergraduate lab training, experiment setup, and data analysis practice.

Publications

I maintain a curated publications page for selected CMS contributions and point to the broader collaboration list for full context.

Education

  • PhD in Physics, UCLouvain — 2026
  • MSc in Physics, Istanbul Technical University — 2022
  • BSc in Astronautical Engineering, Istanbul Technical University — 2019

Skills

  • Python, PyTorch, NumPy, pandas, transformer architectures
  • Data analysis and Machine learning
  • Docker, CI/CD, scalable data pipelines, reproducible tooling
  • Clear technical communication, cross-team collaboration, mentoring

Online

See sidebar on the left.