
Over six months, Jacob Plotnick developed and enhanced data visualization and analysis features for the IMSA-CMS/CMSAnalysis repository, focusing on histogram generation, plotting frameworks, and interactive UI components. He implemented unit-aware histogram axes, refactored plotting logic for maintainability, and expanded support for Higgs and Dark Photon analyses. Using C++, HTML, and JavaScript, Jacob improved error handling, file management, and systematic uncertainty calculations, resulting in more reliable and interpretable analytics dashboards. His work addressed both backend data structures and frontend visualization, demonstrating depth in scientific computing and statistical analysis while ensuring code extensibility and clarity for CMS analysis workflows.

February 2026 — IMSA-CMS/CMSAnalysis: Key feature delivered: Dark Photon Analysis Visualization — UI and Rendering Improvements, including new HTML visualization files with interactive plot toggles; refactored histogram drawing logic; improved systematic calculations for more accurate data visualization. Major bugs fixed: None reported in this repo for this month. Overall impact: Enhanced interpretability and reliability of dark photon visualization, enabling faster insights and more confident data-driven decisions; maintainability improved through refactoring of plotting code. Technologies/skills demonstrated: HTML-based visualization, interactive UI components, plotting refactor, and systematic uncertainty handling in data visualization.
February 2026 — IMSA-CMS/CMSAnalysis: Key feature delivered: Dark Photon Analysis Visualization — UI and Rendering Improvements, including new HTML visualization files with interactive plot toggles; refactored histogram drawing logic; improved systematic calculations for more accurate data visualization. Major bugs fixed: None reported in this repo for this month. Overall impact: Enhanced interpretability and reliability of dark photon visualization, enabling faster insights and more confident data-driven decisions; maintainability improved through refactoring of plotting code. Technologies/skills demonstrated: HTML-based visualization, interactive UI components, plotting refactor, and systematic uncertainty handling in data visualization.
December 2025 (IMSA-CMS/CMSAnalysis): Delivered Dark Photons data visualization enhancements to improve interpretability of histograms and data exploration. Implemented new HTML files for Dark Photons, updated existing visualization assets, and added logarithmic minimum value calculations to enhance histogram scaling. The work was reinforced by a targeted commit focused on plot scaling (53e00101e3d48f4fc5979ec4d77459ea6e4e1561).
December 2025 (IMSA-CMS/CMSAnalysis): Delivered Dark Photons data visualization enhancements to improve interpretability of histograms and data exploration. Implemented new HTML files for Dark Photons, updated existing visualization assets, and added logarithmic minimum value calculations to enhance histogram scaling. The work was reinforced by a targeted commit focused on plot scaling (53e00101e3d48f4fc5979ec4d77459ea6e4e1561).
Month 2025-11: IMSA-CMS/CMSAnalysis delivered robust visualization and histogram capabilities with improved file handling and safer UI, strengthening data reliability for CMS analysis workflows.
Month 2025-11: IMSA-CMS/CMSAnalysis delivered robust visualization and histogram capabilities with improved file handling and safer UI, strengthening data reliability for CMS analysis workflows.
2025-10 — IMSA-CMS/CMSAnalysis: Delivered Higgs signal histogram and invariant mass type support across multiple channels by extending histogram variables and integrating new invariant mass types into existing data structures, enhancing the Higgs analysis workflow. No major bugs reported this month. Overall impact: broadened analysis capabilities, improved data-model compatibility, and readiness for systematic rate/shape evaluations. Technologies/skills demonstrated: histogram analysis, data-structure extension, cross-channel integration, commit-level traceability, and framework integration in the CMSAnalysis codebase (C++/Python).
2025-10 — IMSA-CMS/CMSAnalysis: Delivered Higgs signal histogram and invariant mass type support across multiple channels by extending histogram variables and integrating new invariant mass types into existing data structures, enhancing the Higgs analysis workflow. No major bugs reported this month. Overall impact: broadened analysis capabilities, improved data-model compatibility, and readiness for systematic rate/shape evaluations. Technologies/skills demonstrated: histogram analysis, data-structure extension, cross-channel integration, commit-level traceability, and framework integration in the CMSAnalysis codebase (C++/Python).
September 2025 performance summary for IMSA-CMS/CMSAnalysis. Delivered significant enhancements to histograms and plotting, improving reliability and business value for CMS analysis workflows.
September 2025 performance summary for IMSA-CMS/CMSAnalysis. Delivered significant enhancements to histograms and plotting, improving reliability and business value for CMS analysis workflows.
Month: 2025-08 — Delivered Histogram Axis Unit Support in IMSA-CMS/CMSAnalysis, introducing a unit parameter to HistVariable with storage, a getter, and default unit inference based on variable type. This update improves histogram readability and correctness across analytics dashboards, reducing misinterpretation and manual unit handling. No high-severity bugs reported; changes are backward-compatible and designed for easy extension to additional units. Technical focus: API design, type-aware defaults, and maintainable code patterns. Business impact: clearer visuals, faster data-driven decisions, and improved trust in reported metrics.
Month: 2025-08 — Delivered Histogram Axis Unit Support in IMSA-CMS/CMSAnalysis, introducing a unit parameter to HistVariable with storage, a getter, and default unit inference based on variable type. This update improves histogram readability and correctness across analytics dashboards, reducing misinterpretation and manual unit handling. No high-severity bugs reported; changes are backward-compatible and designed for easy extension to additional units. Technical focus: API design, type-aware defaults, and maintainable code patterns. Business impact: clearer visuals, faster data-driven decisions, and improved trust in reported metrics.
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