aidotse/LeakPro
Nov 2024 – Mar 2025
4 Months active
Languages Used
Jupyter NotebookPythonYAMLGitGit ConfigurationShellSQLBash
Technical Skills
Computer VisionConfiguration ManagementData HandlingData LoadingData PreparationData Preprocessing
fazelehh
PROFILE
Fazelehh
Fazeleh Hoseini contributed to the aidotse/LeakPro repository by building and refining privacy-preserving machine learning workflows, with a focus on healthcare data analysis and attack simulation. Over four months, she integrated the CelebA dataset, established automated data pipelines, and implemented Differentially Private Stochastic Gradient Descent (DP-SGD) for GRU-D models, enabling reproducible and privacy-compliant experiments. Her work involved extensive code refactoring, configuration management, and debugging, using Python, PyTorch, and Jupyter Notebooks. By improving data handling, model training stability, and attack configuration, Fazeleh enhanced both the reliability and maintainability of the codebase, supporting faster experimentation and robust security testing.
Overall Statistics
Feature vs Bugs
62%Features
Repository Contributions
60Total
Bugs
11
Commits
60
Features
18
Lines of code
64,206
Activity Months4
Your Network
13 people
Work History
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.
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
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.
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.
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.
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
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.
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.
Activity
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Quality Metrics
Correctness83.0%
Maintainability83.2%
Architecture77.4%
Performance69.2%
AI Usage24.6%
Skills & Technologies
Programming Languages
BashGitGit ConfigurationJSONJupyter NotebookMarkdownPythonSQLShellYAML
Technical Skills
Attack SimulationCode CleanupCode CommentingCode FormattingCode OrganizationCode RefactoringCode ReviewComputer VisionConfigurationConfiguration ManagementCybersecurityData EngineeringData HandlingData LoadingData Management
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