Undergraduate Training Modules for CERN CMS Data

For my senior thesis, I developed a series of Python-based training modules designed to introduce undergraduate students to data analysis techniques used in high-energy physics. The modules were built using real datasets from CERN’s Compact Muon Solenoid (CMS) experiment, giving students hands-on exposure to particle collision events and modern methods of signal extraction.

The project required not only designing clear, pedagogically effective code workflows but also ensuring that the modules could scale for broader classroom use. To achieve this, I collaborated with researchers across multiple institutions, refining the materials based on feedback and aligning them with diverse instructional needs.

This work combined computational development with scientific communication, bridging advanced particle physics concepts with accessible programming exercises. By transforming raw CMS data into structured learning tools, the project demonstrated how Python-driven analysis, cross-institution collaboration, and applied physics research can come together to support the next generation of scientists.