بعد أكاديمية كاوست
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.
المشروع الصيفي
تخصص الذكاء الاصطناعي · دفعة 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.
مع أكاديمية كاوست
- دفعة 2026
المنظمة الأوروبية للأبحاث النووية (CERN) · التدريب الصيفي في الذكاء الاصطناعي
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