After KAUST Academy
Mozoon spent two months at CERN in summer 2026 as part of the KAUST Academy AI program, working on unsupervised clustering for irregular detector regions within the CMS Collaboration. Mozoon presented the project at the KAUST Academy closing ceremony poster day in September 2026.
Summer project
Artificial Intelligence Specialization · Cohort 2026
Unsupervised learning for robust clustering in irregular detector regions
Particle detectors such as the CMS are not perfectly uniform systems and can contain gaps, cracks, or faulty modules. These irregular regions can make pattern recognition techniques less efficient. Clustering techniques such as CLUEstering are suitable for these tasks because they can be robust to irregular and noisy data. However, detector data can contain many features, which can make clustering more complex. Dimensionality reduction techniques can be used to reduce the number of features and potentially make clustering more efficient. Therefore, this project investigates whether dimensionality reduction methods such as UMAP can improve the robustness of clustering techniques in irregular detector regions.
With KAUST Academy
- Cohort 2026
Artificial Intelligence Specialization
CERN · Artificial Intelligence Summer Internship
Want your entry updated or removed? Contact us.