madeline-underwood/arm-learning-paths
Nov 2024 – Dec 2025
10 Months active
Languages Used
BashC#KotlinMarkdownPythonXMLCSSHTML
Technical Skills
.NET.NET AspireAWSAndroid DevelopmentArm ArchitectureCloud Computing
Dawid Borycki
PROFILE
Dawid Borycki
Over ten months, Dawid Borycki developed end-to-end machine learning and computer vision pipelines for the madeline-underwood/arm-learning-paths repository, focusing on ARM and Android platforms. He engineered on-device Sudoku solvers, real-time image processing with Halide and OpenCV, and cross-cloud deployment learning paths, emphasizing performance and maintainability. Dawid integrated ONNX Runtime for portable inference, optimized ARM64 workflows, and created synthetic data generators to accelerate model training. His work combined C++, Python, and Kotlin, with detailed documentation and onboarding guides. The solutions reduced cloud dependency, improved latency, and enabled scalable, offline ML pipelines, demonstrating depth in both technical implementation and developer enablement.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
45Total
Bugs
0
Commits
45
Features
18
Lines of code
12,279
Activity Months10
Your Network
129 peopleWork History
December 2025
8 Commits • 1 Features
Dec 1, 2025December 2025 performance summary for madeline-underwood/arm-learning-paths. Delivered a complete on-device Sudoku processing pipeline for Android using ONNX Runtime, enabling offline grid detection, digit recognition, and solving on Arm64 devices. Achieved substantial performance optimizations, refined the Android build and lifecycle, and updated the learning path/docs to accelerate team adoption and contributor onboarding. This work reduces cloud inference dependency, improves latency, and establishes a scalable foundation for mobile ML pipelines.
8 Commits • 1 Features
Dec 1, 2025December 2025 performance summary for madeline-underwood/arm-learning-paths. Delivered a complete on-device Sudoku processing pipeline for Android using ONNX Runtime, enabling offline grid detection, digit recognition, and solving on Arm64 devices. Achieved substantial performance optimizations, refined the Android build and lifecycle, and updated the learning path/docs to accelerate team adoption and contributor onboarding. This work reduces cloud inference dependency, improves latency, and establishes a scalable foundation for mobile ML pipelines.
December 2025
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for madeline-underwood/arm-learning-paths: focused on delivering portable ML and real-time image-processing capabilities, addressing performance and maintainability, and documenting the workflow.
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for madeline-underwood/arm-learning-paths: focused on delivering portable ML and real-time image-processing capabilities, addressing performance and maintainability, and documenting the workflow.
September 2025
5 Commits • 3 Features
Sep 1, 20252025-09 Monthly Summary: Focused on delivering end-to-end ML experimentation and deployment readiness on Arm-enabled platforms. Implemented real-time Halide fusion demos, created an Android ONNX learning path with simplified Arm64 setup, and introduced a synthetic Sudoku dataset generator to accelerate digit-recognition model training. No major defects reported; work emphasizes business value through faster experimentation, clearer documentation, and reusable data tooling that scales across deployments.
5 Commits • 3 Features
Sep 1, 20252025-09 Monthly Summary: Focused on delivering end-to-end ML experimentation and deployment readiness on Arm-enabled platforms. Implemented real-time Halide fusion demos, created an Android ONNX learning path with simplified Arm64 setup, and introduced a synthetic Sudoku dataset generator to accelerate digit-recognition model training. No major defects reported; work emphasizes business value through faster experimentation, clearer documentation, and reusable data tooling that scales across deployments.
September 2025
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for madeline-underwood/arm-learning-paths focused on delivering Halide Operation Fusion and Tiling Optimizations for the Android Graphics Pipeline. The work included documentation consolidation, practical code examples, and optimizations aimed at enhancing scheduling, cache efficiency, and overall graphics performance in Android deployments.
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for madeline-underwood/arm-learning-paths focused on delivering Halide Operation Fusion and Tiling Optimizations for the Android Graphics Pipeline. The work included documentation consolidation, practical code examples, and optimizations aimed at enhancing scheduling, cache efficiency, and overall graphics performance in Android deployments.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered substantial documentation enhancements for Halide Android learning path and image processing pipeline, addressing reviewer feedback across two commits. Resulted in clearer prerequisites, external resources guidance, cross-compilation steps, type casting and scheduling concepts, and expanded image processing pipeline with grayscale and Gaussian blur examples, plus detailed JNI buffer handling, installation and build instructions. Reduced onboarding friction and potential build issues, enabling faster developer adoption and more reliable Android integration of Halide.
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered substantial documentation enhancements for Halide Android learning path and image processing pipeline, addressing reviewer feedback across two commits. Resulted in clearer prerequisites, external resources guidance, cross-compilation steps, type casting and scheduling concepts, and expanded image processing pipeline with grayscale and Gaussian blur examples, plus detailed JNI buffer handling, installation and build instructions. Reduced onboarding friction and potential build issues, enabling faster developer adoption and more reliable Android integration of Halide.
June 2025
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for madeline-underwood/arm-learning-paths: Delivered two comprehensive learning-path features: Halide on Android and ONNX Runtime learning path for Windows ARM with Python. Implemented a real-time image processing pipeline and Kotlin/JNI integration, including NDK setup, ARM cross-compilation, and data flow between Android Bitmaps and Halide buffers. Added ONNX Runtime learning path for Windows ARM with Python, including setup instructions, virtual environments, MNIST inference example, and refined inference steps. Consolidated Halide education content and improved deployment docs (fusion.md, AOT, Android) and ONNX inference docs. No major bugs reported; addressed setup-related issues and improved documentation. Technologies demonstrated include Halide, Android NDK, Kotlin/JNI, ARM cross-compilation, ONNX Runtime, Python, and MNIST inference workflows. Business value: accelerated ARM-based learning content, enabled real-time processing capabilities for Android apps, and clarified cross-platform deployment workflows, reducing developer onboarding time.
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for madeline-underwood/arm-learning-paths: Delivered two comprehensive learning-path features: Halide on Android and ONNX Runtime learning path for Windows ARM with Python. Implemented a real-time image processing pipeline and Kotlin/JNI integration, including NDK setup, ARM cross-compilation, and data flow between Android Bitmaps and Halide buffers. Added ONNX Runtime learning path for Windows ARM with Python, including setup instructions, virtual environments, MNIST inference example, and refined inference steps. Consolidated Halide education content and improved deployment docs (fusion.md, AOT, Android) and ONNX inference docs. No major bugs reported; addressed setup-related issues and improved documentation. Technologies demonstrated include Halide, Android NDK, Kotlin/JNI, ARM cross-compilation, ONNX Runtime, Python, and MNIST inference workflows. Business value: accelerated ARM-based learning content, enabled real-time processing capabilities for Android apps, and clarified cross-platform deployment workflows, reducing developer onboarding time.
March 2025
12 Commits • 3 Features
Mar 1, 2025Monthly summary for 2025-03 - madeline-underwood/arm-learning-paths: Key features delivered: - Azure IoT Learning Path: Foundation, Objectives, and Documentation. Established and documented the Azure IoT learning path with initial setup, learning objectives, prerequisites, and comprehensive guidance for Arm64 IoT deployments. Commits contributing: f18e22873dc1c1df5b27a9c15de769054b0d6839; 185d59a6f1c88990a996834d6bbeca2473d214ad; 79127b8dd6ba2b5faf1ac2a3648b422332618402; 6d6e318f1cebf988108684bcab9386fb2beb98b9; cc7647edbd0063d5a81449fd52cb69f226958b45. - IoT Data Simulator: Python-based simulator to generate and stream sensor data to Azure IoT Hub for testing and visualization. Commit: 18f1e23692533d8b4a3ea0fe4efa24a8a197f5da. - Real-time IoT Analytics Platform with Visualization: End-to-end analytics pipeline for IoT data using Azure Stream Analytics, Cosmos DB, Functions for monitoring and alerting, and a user-facing visualization portal. Commits: 9ef30ac9aeea7f646163b135a986e56121567b6e; a42286e5cde94f598e24be3dc1acd568adaa7488; 6d0ee164e6163052280cb8f5617f5540f3086742; e59feb183f779f3f320f76b1c90207adde7ece44; a7d47523568273b80c81a76cceb459772c0e2858; 95aa749589b3924ff70a9a19408b078465f93dab. Major bugs fixed: - Resolved data consistency and reliability issues in Cosmos DB integration and the real-time pipeline. Specific fixes included stabilization of data ingestion paths and query reliability. Related commits include a42286e5cde94f598e24be3dc1acd568adaa7488 and 6d0ee164e6163052280cb8f5617f5540f3086742. - Improved aggregation correctness and error handling in the analytics layer (Stream Analytics and Functions) to ensure accurate real-time insights. Related commits: e59feb183f779f3f320f76b1c90207adde7ece44 and 95aa749589b3924ff70a9a19408b078465f93dab. Overall impact and accomplishments: - Delivered end-to-end IoT capability for Arm64, from data generation to real-time analytics and visualization, enabling faster validation, testing, and decision-making. - Accelerated onboarding for Arm64 IoT deployments through a comprehensive learning path and up-to-date documentation. - Established proactive monitoring and alerting in production-like flows, improving incident response and operational visibility. Technologies/skills demonstrated: - Cloud IoT: Azure IoT Hub, Arm64 IoT deployments - Data generation and testing: Python-based IoT Data Simulator - Real-time analytics stack: Azure Stream Analytics, Cosmos DB, Azure Functions, data visualization portal - Data engineering and DevOps practices: end-to-end pipeline design, commits-driven development, documentation improvements
12 Commits • 3 Features
Mar 1, 2025Monthly summary for 2025-03 - madeline-underwood/arm-learning-paths: Key features delivered: - Azure IoT Learning Path: Foundation, Objectives, and Documentation. Established and documented the Azure IoT learning path with initial setup, learning objectives, prerequisites, and comprehensive guidance for Arm64 IoT deployments. Commits contributing: f18e22873dc1c1df5b27a9c15de769054b0d6839; 185d59a6f1c88990a996834d6bbeca2473d214ad; 79127b8dd6ba2b5faf1ac2a3648b422332618402; 6d6e318f1cebf988108684bcab9386fb2beb98b9; cc7647edbd0063d5a81449fd52cb69f226958b45. - IoT Data Simulator: Python-based simulator to generate and stream sensor data to Azure IoT Hub for testing and visualization. Commit: 18f1e23692533d8b4a3ea0fe4efa24a8a197f5da. - Real-time IoT Analytics Platform with Visualization: End-to-end analytics pipeline for IoT data using Azure Stream Analytics, Cosmos DB, Functions for monitoring and alerting, and a user-facing visualization portal. Commits: 9ef30ac9aeea7f646163b135a986e56121567b6e; a42286e5cde94f598e24be3dc1acd568adaa7488; 6d0ee164e6163052280cb8f5617f5540f3086742; e59feb183f779f3f320f76b1c90207adde7ece44; a7d47523568273b80c81a76cceb459772c0e2858; 95aa749589b3924ff70a9a19408b078465f93dab. Major bugs fixed: - Resolved data consistency and reliability issues in Cosmos DB integration and the real-time pipeline. Specific fixes included stabilization of data ingestion paths and query reliability. Related commits include a42286e5cde94f598e24be3dc1acd568adaa7488 and 6d0ee164e6163052280cb8f5617f5540f3086742. - Improved aggregation correctness and error handling in the analytics layer (Stream Analytics and Functions) to ensure accurate real-time insights. Related commits: e59feb183f779f3f320f76b1c90207adde7ece44 and 95aa749589b3924ff70a9a19408b078465f93dab. Overall impact and accomplishments: - Delivered end-to-end IoT capability for Arm64, from data generation to real-time analytics and visualization, enabling faster validation, testing, and decision-making. - Accelerated onboarding for Arm64 IoT deployments through a comprehensive learning path and up-to-date documentation. - Established proactive monitoring and alerting in production-like flows, improving incident response and operational visibility. Technologies/skills demonstrated: - Cloud IoT: Azure IoT Hub, Arm64 IoT deployments - Data generation and testing: Python-based IoT Data Simulator - Real-time analytics stack: Azure Stream Analytics, Cosmos DB, Azure Functions, data visualization portal - Data engineering and DevOps practices: end-to-end pipeline design, commits-driven development, documentation improvements
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for madeline-underwood/arm-learning-paths: Delivered Android OpenCV KleidiCV Learning Path Documentation and Project Setup Improvements. Result: clearer documentation, corrected Android package name in the setup, refreshed image processing details, and refined UI paths and performance uplift notes to reduce onboarding time and build friction.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for madeline-underwood/arm-learning-paths: Delivered Android OpenCV KleidiCV Learning Path Documentation and Project Setup Improvements. Result: clearer documentation, corrected Android package name in the setup, refreshed image processing details, and refined UI paths and performance uplift notes to reduce onboarding time and build friction.
January 2025
3 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for madeline-underwood/arm-learning-paths. Delivered foundational OpenCV Android Learning Path with KleidiCV acceleration, including initial project scaffold, UI, content, and Android Studio configuration. Subsequent iterations refined image processing details, integrated performance metrics, and updated figures to demonstrate acceleration. Enhanced discoverability by adding a direct GitHub repository link. Overall impact: created a business-ready learning module that accelerates developer onboarding for Android computer vision and provides measurable performance gains, setting the stage for further refinements and broader adoption.
3 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for madeline-underwood/arm-learning-paths. Delivered foundational OpenCV Android Learning Path with KleidiCV acceleration, including initial project scaffold, UI, content, and Android Studio configuration. Subsequent iterations refined image processing details, integrated performance metrics, and updated figures to demonstrate acceleration. Enhanced discoverability by adding a direct GitHub repository link. Overall impact: created a business-ready learning module that accelerates developer onboarding for Android computer vision and provides measurable performance gains, setting the stage for further refinements and broader adoption.
January 2025
November 2024
2 Commits • 2 Features
Nov 1, 2024November 2024: Focused on building practical, cross-cloud learning paths for ARM deployments and mobile inference optimization in the arm-learning-paths repository. Delivered two new learning paths with clear deployment, performance testing, and optimization guidance, enabling developers to deploy .NET Aspire apps to ARM-powered VMs on AWS and GCP and to run and optimize PyTorch digit classification on Android.
November 2024
2 Commits • 2 Features
Nov 1, 2024November 2024: Focused on building practical, cross-cloud learning paths for ARM deployments and mobile inference optimization in the arm-learning-paths repository. Delivered two new learning paths with clear deployment, performance testing, and optimization guidance, enabling developers to deploy .NET Aspire apps to ARM-powered VMs on AWS and GCP and to run and optimize PyTorch digit classification on Android.
Activity
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Quality Metrics
Correctness91.8%
Maintainability89.6%
Architecture90.0%
Performance87.0%
AI Usage25.4%
Skills & Technologies
Programming Languages
BashC#C++CSSConsoleGradleHTMLHalideJSONJava
Technical Skills
.NET.NET AspireAOT CompilationARM ArchitectureAWSAlertingAndroid DevelopmentAndroid developmentArm ArchitectureArm64AzureAzure Cosmos DBAzure FunctionsAzure IoT HubAzure Stream Analytics
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline