DUNE/dunereco
Nov 2025 – Jun 2026
2 Months active
Languages Used
JSONJSONNETJavaScript
Technical Skills
configuration managementdata analysisdata configurationdata processingnoise filteringscientific computing
Xuyang Ning
PROFILE
Xuyang Ning
Worked on the DUNE/dunereco repository to deliver advanced signal processing and machine learning integration for high energy physics applications. Developed and configured a DNN ROI-driven signal processing framework, enabling neural network-based region-of-interest processing across multiple detectors. Enhanced noise filtering by refining channel-group configurations and extending RMS thresholds, improving noisy channel tagging. Implemented dual-output persistence to support both traditional and ML-augmented outputs, increasing traceability for downstream analysis. Optimized build infrastructure using CMake and improved memory management within the data pipeline. Leveraged C++, JSONNet, and configuration management skills to establish a scalable, maintainable foundation for future ML-driven enhancements in scientific computing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
4
Lines of code
3,615
Activity Months2
Your Network
30 peopleShared Repositories
30
Aaron HigueraMember
Alex WilkinsonMember
AndyChappellMember
Andrew OlivierMember
Avik GhoshMember
Alejandro YankelevichMember
Jacob CalcuttMember
Dom BrailsfordMember
emanuele-villaMember
Work History
June 2026
10 Commits • 2 Features
Jun 1, 2026June 2026 performance summary for DUNE/dunereco: - Delivered foundational DNN ROI-driven signal processing framework and dual-output persistence, enabling ML-assisted ROI processing across detectors (PDHD, PDVD, ProtoDUNE-HD/VD). - Implemented infrastructure and configuration for DNN ROI, L1SP, and ML-based signal processing. Core work includes enabling DNN ROI and L1SP in a hybrid pipeline, with model and path management to support ongoing development. - Built reliability and performance improvements through build/config optimizations (CMake rules for TypeScript files, removal of unused CMake rules) and memory optimization (TagSelector to reduce memory footprint). - Enabled dual-output persistence to save both traditional signal-processing outputs and post-DNN ROI results by routing outputs to a frame saver, improving traceability and downstream analytics. - Strengthened reliability and model management with fixes for PDVD DNN-ROI models, PDHD nticks stabilization, HuggingFace model path updates, and NF-based initialization configurations (start from NF result) along with updates to wcls-sp-dnn-l1sp.jsonnet. Impact: - Accelerated ML-enabled ROI processing with scalable infrastructure across detectors, improved signal-quality follow-through, and reduced memory usage. Established a solid foundation for production-grade DNN ROI adoption and easier future enhancements. Technologies/skills demonstrated: - DNN ROI/L1SP integration, frame saver, JSONNET, HuggingFace model management, CMake/build optimization, TypeScript tooling, ML-based signal processing workflows.
10 Commits • 2 Features
Jun 1, 2026June 2026 performance summary for DUNE/dunereco: - Delivered foundational DNN ROI-driven signal processing framework and dual-output persistence, enabling ML-assisted ROI processing across detectors (PDHD, PDVD, ProtoDUNE-HD/VD). - Implemented infrastructure and configuration for DNN ROI, L1SP, and ML-based signal processing. Core work includes enabling DNN ROI and L1SP in a hybrid pipeline, with model and path management to support ongoing development. - Built reliability and performance improvements through build/config optimizations (CMake rules for TypeScript files, removal of unused CMake rules) and memory optimization (TagSelector to reduce memory footprint). - Enabled dual-output persistence to save both traditional signal-processing outputs and post-DNN ROI results by routing outputs to a frame saver, improving traceability and downstream analytics. - Strengthened reliability and model management with fixes for PDVD DNN-ROI models, PDHD nticks stabilization, HuggingFace model path updates, and NF-based initialization configurations (start from NF result) along with updates to wcls-sp-dnn-l1sp.jsonnet. Impact: - Accelerated ML-enabled ROI processing with scalable infrastructure across detectors, improved signal-quality follow-through, and reduced memory usage. Established a solid foundation for production-grade DNN ROI adoption and easier future enhancements. Technologies/skills demonstrated: - DNN ROI/L1SP integration, frame saver, JSONNET, HuggingFace model management, CMake/build optimization, TypeScript tooling, ML-based signal processing workflows.
June 2026
November 2025
3 Commits • 2 Features
Nov 1, 2025November 2025 month-in-review for the DUNE/dunereco repository focused on delivering robust noise filtering capabilities and enabling neural network processing, with clear, traceable commits to support future ML-driven enhancements and maintainability.
November 2025
3 Commits • 2 Features
Nov 1, 2025November 2025 month-in-review for the DUNE/dunereco repository focused on delivering robust noise filtering capabilities and enabling neural network processing, with clear, traceable commits to support future ML-driven enhancements and maintainability.
Activity
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Quality Metrics
Correctness95.4%
Maintainability84.6%
Architecture86.2%
Performance83.0%
AI Usage50.8%
Skills & Technologies
Programming Languages
JSONJSONNETJavaScript
Technical Skills
Build EngineeringC++CMakeCVMFSConfiguration ManagementData AnalysisData Pipeline OptimizationData ProcessingData ReconstructionFHiCLHigh Energy Physics SoftwareInfrastructureJSONNetJsonnetMachine Learning
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