
Over six months, contributed to the LDMX-Software/ldmx-sw repository by developing and refining machine learning-driven event veto and analysis pipelines for detector simulation. Built end-to-end Boosted Decision Tree workflows, integrating Python-based model training with C++ event processing, and enhanced configuration management for maintainable, data-driven veto logic. Improved analysis fidelity through new histograms, ROC curve enablement, and validation tools, while stabilizing the codebase with targeted bug fixes and modular refactoring. Leveraged CMake for build integration and applied rigorous code organization, enabling robust signal/background discrimination and streamlined deployment. The work emphasized maintainability, numerical correctness, and cross-language integration for scientific computing.
Month 2025-08 focused on maintainability and validation fidelity in LDMX-Software/ldmx-sw. Delivered code quality cleanup and module reorganization for Hcal/Visibles, along with validation enhancements for Visibles BDT analysis. No critical bugs closed this month; the work reduces technical debt and strengthens future deployability and test coverage.
Month 2025-08 focused on maintainability and validation fidelity in LDMX-Software/ldmx-sw. Delivered code quality cleanup and module reorganization for Hcal/Visibles, along with validation enhancements for Visibles BDT analysis. No critical bugs closed this month; the work reduces technical debt and strengthens future deployability and test coverage.
Month: 2025-07. This period delivered key features and bug fixes across the LDMX-Software/ldmx-sw repository, driving robust analysis capabilities and improved veto performance. Major outcomes include ROC analysis enablement for cutflow, targeted numerical stability fixes in VisiblesCutflow and HCAL analysis, and a visible veto ML model upgrade with versioned assets and ONNX alignment. These changes collectively enhance measurement fidelity, reduce risk in data processing, and demonstrate strong capability in algorithm development, numerical correctness, and model deployment.
Month: 2025-07. This period delivered key features and bug fixes across the LDMX-Software/ldmx-sw repository, driving robust analysis capabilities and improved veto performance. Major outcomes include ROC analysis enablement for cutflow, targeted numerical stability fixes in VisiblesCutflow and HCAL analysis, and a visible veto ML model upgrade with versioned assets and ONNX alignment. These changes collectively enhance measurement fidelity, reduce risk in data processing, and demonstrate strong capability in algorithm development, numerical correctness, and model deployment.
June 2025: Delivered key features and stability improvements in LDMX-Software/ldmx-sw focused on beam analysis capabilities, data access, and testing/observability. These efforts improved analysis fidelity, debugging efficiency, and overall production reliability, aligning with the team's objective to accelerate physics results and ensure robust data workflows.
June 2025: Delivered key features and stability improvements in LDMX-Software/ldmx-sw focused on beam analysis capabilities, data access, and testing/observability. These efforts improved analysis fidelity, debugging efficiency, and overall production reliability, aligning with the team's objective to accelerate physics results and ensure robust data workflows.
May 2025 | Repository: LDMX-Software/ldmx-sw Key features delivered - HCAL Visible Veto: training architecture and integration. Introduced VisiblesFeatureProducer for BDT training data generation and refactored VisiblesVetoProcessor to delegate training. Added VisiblesVetoResult to the build system; BDT threshold configured. - HCAL Visible event filtering and analysis enhancements. Added recoil-based event cut, additional histograms, and a new VisiblesCutflow class to analyze visible particles and apply cuts/BDT scores for improved signal/background discrimination. Major bugs fixed - Bug: Fix compilation issues in VisiblesVetoProcessor. Resolved function name mismatches, include directives, and parameter handling to enable compilation and integration into the event processing framework. Overall impact and accomplishments - Stabilized and extended the HCAL Visible Veto pipeline, enabling end-to-end training data generation and evaluation within the existing event processing framework. - Improved signal/background discrimination through targeted analysis enhancements and a dedicated Visibles analysis workflow, setting the stage for performance gains in downstream physics results. Technologies/skills demonstrated - Codebase refactoring and modularization (training logic moved to VisiblesFeatureProducer) - Build-system integration (CMake) with new VisiblesVetoResult and training components - BDT training data generation and threshold tuning - Recoil-based cuts, histogram instrumentation, and VisiblesCutflow analytics
May 2025 | Repository: LDMX-Software/ldmx-sw Key features delivered - HCAL Visible Veto: training architecture and integration. Introduced VisiblesFeatureProducer for BDT training data generation and refactored VisiblesVetoProcessor to delegate training. Added VisiblesVetoResult to the build system; BDT threshold configured. - HCAL Visible event filtering and analysis enhancements. Added recoil-based event cut, additional histograms, and a new VisiblesCutflow class to analyze visible particles and apply cuts/BDT scores for improved signal/background discrimination. Major bugs fixed - Bug: Fix compilation issues in VisiblesVetoProcessor. Resolved function name mismatches, include directives, and parameter handling to enable compilation and integration into the event processing framework. Overall impact and accomplishments - Stabilized and extended the HCAL Visible Veto pipeline, enabling end-to-end training data generation and evaluation within the existing event processing framework. - Improved signal/background discrimination through targeted analysis enhancements and a dedicated Visibles analysis workflow, setting the stage for performance gains in downstream physics results. Technologies/skills demonstrated - Codebase refactoring and modularization (training logic moved to VisiblesFeatureProducer) - Build-system integration (CMake) with new VisiblesVetoResult and training components - BDT training data generation and threshold tuning - Recoil-based cuts, histogram instrumentation, and VisiblesCutflow analytics
February 2025 monthly summary for LDMX-Software/ldmx-sw: Focused on delivering foundational enhancements to HCAL event processing and establishing a machine learning-based event classifier pipeline. No major bug fixes were documented for this period; minor workflow tweaks were implemented to streamline HCAL processing.
February 2025 monthly summary for LDMX-Software/ldmx-sw: Focused on delivering foundational enhancements to HCAL event processing and establishing a machine learning-based event classifier pipeline. No major bug fixes were documented for this period; minor workflow tweaks were implemented to streamline HCAL processing.
January 2025 monthly summary for LDMX-Software/ldmx-sw focusing on bug fixes and ML integration in VisiblesVetoProcessor. Key themes: accuracy improvements, data-driven veto via BDTs, and maintainable configuration-driven workflows.
January 2025 monthly summary for LDMX-Software/ldmx-sw focusing on bug fixes and ML integration in VisiblesVetoProcessor. Key themes: accuracy improvements, data-driven veto via BDTs, and maintainable configuration-driven workflows.

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