aidotse/LeakPro
Nov 2024 – Mar 2026
7 Months active
Languages Used
Jupyter NotebookPythonYAMLGitGit ConfigurationShellSQLBash
Technical Skills
Computer VisionConfiguration ManagementData HandlingData LoadingData PreparationData Preprocessing
fazelehh
PROFILE
Fazelehh
Fazeleh Hoseini developed core privacy and machine learning features for the LeakPro repository, focusing on attack simulation, model auditing, and robust data workflows. Over seven months, she integrated differential privacy into healthcare analytics, unified multiple attack strategies, and enhanced model training pipelines using Python and PyTorch. Her work included refactoring for maintainability, automating data pipelines, and improving configuration management to support reproducible experiments. She strengthened project hygiene with improved documentation, CI workflows, and onboarding resources. Hoseini’s contributions addressed privacy compliance, accelerated experimentation, and enabled reliable risk assessment, demonstrating depth in data privacy, deep learning, and collaborative software engineering practices.
Overall Statistics
Feature vs Bugs
69%Features
Repository Contributions
79Total
Bugs
12
Commits
79
Features
27
Lines of code
1,459,539
Activity Months7
Your Network
14 peopleWork History
March 2026
8 Commits • 2 Features
Mar 1, 2026March 2026: Delivered core feature enhancements to LeakPro's GRUD training pipeline with OSLO attack refinements, added differential privacy options, strengthened parameter validation, expanded test coverage, and improved contributor onboarding via CI and documentation. These efforts increase model robustness, security, and developer throughput while reducing integration friction for external contributors.
8 Commits • 2 Features
Mar 1, 2026March 2026: Delivered core feature enhancements to LeakPro's GRUD training pipeline with OSLO attack refinements, added differential privacy options, strengthened parameter validation, expanded test coverage, and improved contributor onboarding via CI and documentation. These efforts increase model robustness, security, and developer throughput while reducing integration friction for external contributors.
March 2026
February 2026
6 Commits • 4 Features
Feb 1, 2026February 2026 monthly summary for aidotse/LeakPro: Delivered core feature enhancements, improved model auditing, and strengthened release automation. Key milestones include OSLO attack configuration and enhanced auditing logging, improved LeakPro documentation with runnable instructions and a sample config, performance-oriented code cleanup to accelerate evaluation, and CI/templates/workflow improvements to streamline testing and releases. Impact includes improved auditing accuracy, faster evaluation due to optimized code paths, easier onboarding via enhanced docs, and strengthened release hygiene through templates.
February 2026
6 Commits • 4 Features
Feb 1, 2026February 2026 monthly summary for aidotse/LeakPro: Delivered core feature enhancements, improved model auditing, and strengthened release automation. Key milestones include OSLO attack configuration and enhanced auditing logging, improved LeakPro documentation with runnable instructions and a sample config, performance-oriented code cleanup to accelerate evaluation, and CI/templates/workflow improvements to streamline testing and releases. Impact includes improved auditing accuracy, faster evaluation due to optimized code paths, easier onboarding via enhanced docs, and strengthened release hygiene through templates.
January 2026
5 Commits • 3 Features
Jan 1, 2026January 2026 (Month: 2026-01) delivered a set of business-value features and quality improvements for LeakPro, with targeted fixes to maintain clarity and test coverage. The work emphasizes integration, robustness, and tooling to support ongoing development and risk assessment capabilities.
5 Commits • 3 Features
Jan 1, 2026January 2026 (Month: 2026-01) delivered a set of business-value features and quality improvements for LeakPro, with targeted fixes to maintain clarity and test coverage. The work emphasizes integration, robustness, and tooling to support ongoing development and risk assessment capabilities.
January 2026
March 2025
10 Commits • 4 Features
Mar 1, 2025March 2025 — Privacy and security-focused feature delivery for aidotse/LeakPro. Implemented DP-SGD integration for the GRU-D Length-of-Stay (LoS) model to enable privacy-preserving training in healthcare data analysis, and improved the MIMIC GRUD DPSGD notebook with clearer DP hyperparameters and markdown explanations. Fixed critical DPSGD flag logic to ensure correct LeakPro handler instantiation, refined RMIA attack configuration for robustness and data-type correctness, and enhanced HopSkipJump (HSJ) UX with a progress bar and batch-size sanity checks. Also improved data handling and project hygiene (ignored data paths, type hints, README guidance) and removed deprecated example suite to clean the codebase. These changes contribute to privacy compliance, reliable security testing, and faster developer onboarding.
March 2025
10 Commits • 4 Features
Mar 1, 2025March 2025 — Privacy and security-focused feature delivery for aidotse/LeakPro. Implemented DP-SGD integration for the GRU-D Length-of-Stay (LoS) model to enable privacy-preserving training in healthcare data analysis, and improved the MIMIC GRUD DPSGD notebook with clearer DP hyperparameters and markdown explanations. Fixed critical DPSGD flag logic to ensure correct LeakPro handler instantiation, refined RMIA attack configuration for robustness and data-type correctness, and enhanced HopSkipJump (HSJ) UX with a progress bar and batch-size sanity checks. Also improved data handling and project hygiene (ignored data paths, type hints, README guidance) and removed deprecated example suite to clean the codebase. These changes contribute to privacy compliance, reliable security testing, and faster developer onboarding.
February 2025
12 Commits • 4 Features
Feb 1, 2025February 2025 monthly work summary for aidotse/LeakPro focused on delivering robust model training improvements, refactoring for stability, and enhanced observability. Key efforts include LOS and GRUD model enhancements, metrics correctness across handlers, DP-SGD experimentation adjustments with a companion notebook, and auditing/configuration improvements. These work streamlines training, improves performance signals, and strengthens code quality and compliance readiness, positioning the project for faster experimentation and more reliable deployments.
12 Commits • 4 Features
Feb 1, 2025February 2025 monthly work summary for aidotse/LeakPro focused on delivering robust model training improvements, refactoring for stability, and enhanced observability. Key efforts include LOS and GRUD model enhancements, metrics correctness across handlers, DP-SGD experimentation adjustments with a companion notebook, and auditing/configuration improvements. These work streamlines training, improves performance signals, and strengthens code quality and compliance readiness, positioning the project for faster experimentation and more reliable deployments.
February 2025
January 2025
28 Commits • 8 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for aidotse/LeakPro. Focused on stabilizing the project, delivering automated data workflows, and enabling reproducible experiments. Key outcomes include a major repo refactor, dataset download capability, reporting workflow, notebook results finalization, and DPSGD experiment scaffolding. Alongside these, a series of bug fixes improved data pipeline reliability, data path resolution, PDF generation, environment stability, and gitignore/data handling hygiene. These efforts deliver measurable business value: faster iteration cycles, safer data handling, consistent builds, and clearer project structure.
January 2025
28 Commits • 8 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for aidotse/LeakPro. Focused on stabilizing the project, delivering automated data workflows, and enabling reproducible experiments. Key outcomes include a major repo refactor, dataset download capability, reporting workflow, notebook results finalization, and DPSGD experiment scaffolding. Alongside these, a series of bug fixes improved data pipeline reliability, data path resolution, PDF generation, environment stability, and gitignore/data handling hygiene. These efforts deliver measurable business value: faster iteration cycles, safer data handling, consistent builds, and clearer project structure.
November 2024
10 Commits • 2 Features
Nov 1, 2024November 2024 - Focused on establishing foundational CelebA data integration for LeakPro and enabling end-to-end CelebA workflows across LeakPro and MIA examples. Implemented scaffolding with input handling, data preparation utilities, and placeholder model wiring to enable CelebA-based ML tasks; extended CelebA dataset class and loading utilities across the repo; fixed initialization TypeError and path handling in CelebA example, with CIFAR whitespace cleanup to maintain consistency. These efforts deliver a repeatable CelebA experimentation pipeline, improved data reliability, and better cross-example maintainability, setting the stage for production-grade features and faster experimentation. Technologies demonstrated include PyTorch dataset pipelines, custom input handlers, data loading/processing utilities, configuration management, and cross-component debugging.
10 Commits • 2 Features
Nov 1, 2024November 2024 - Focused on establishing foundational CelebA data integration for LeakPro and enabling end-to-end CelebA workflows across LeakPro and MIA examples. Implemented scaffolding with input handling, data preparation utilities, and placeholder model wiring to enable CelebA-based ML tasks; extended CelebA dataset class and loading utilities across the repo; fixed initialization TypeError and path handling in CelebA example, with CIFAR whitespace cleanup to maintain consistency. These efforts deliver a repeatable CelebA experimentation pipeline, improved data reliability, and better cross-example maintainability, setting the stage for production-grade features and faster experimentation. Technologies demonstrated include PyTorch dataset pipelines, custom input handlers, data loading/processing utilities, configuration management, and cross-component debugging.
November 2024
Activity
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Quality Metrics
Correctness85.0%
Maintainability84.8%
Architecture80.2%
Performance73.8%
AI Usage25.8%
Skills & Technologies
Programming Languages
BashGitGit ConfigurationJSONJupyter NotebookMarkdownPythonSQLShellYAML
Technical Skills
Attack SimulationCode CleanupCode CommentingCode FormattingCode OrganizationCode RefactoringCode ReviewComputer VisionConfigurationConfiguration ManagementCybersecurityData AnalysisData EngineeringData HandlingData Loading
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