Open Source

Contributing to the ML and data science community through open source projects, research organizations, and educational resources.

Main Repositories

Deep-Stonks-Group

Research organization exploring algorithmic trading and time-series modeling with automated execution systems.

Machine LearningTime SeriesTradingResearch

Personal Repositories

Collection of ML projects, data engineering tools, and experimental research implementations.

PythonMachine LearningData EngineeringResearch

Contribution Areas

Focus areas for open source contributions and community involvement.

ML Systems & Frameworks

Contributions to production ML pipelines, model serving, and data processing frameworks.

PyTorchTensorFlowScikit-learnMLOps

Computer Vision Tools

Open source tools for medical imaging, histopathology analysis, and image processing workflows.

OpenCVPILHistomicsTKMedical Imaging

Data Engineering

Scalable data pipelines, ETL frameworks, and automation tools for large-scale processing.

Apache SparkAWSData PipelinesAutomation

Educational Resources

Teaching materials, tutorials, and course content for AI and machine learning education.

JupyterDocumentationTutorialsCurriculum

Open Source Philosophy

Open source drives innovation through collaboration. Every contribution should advance the field, whether through novel research, production-ready tools, or educational resources that help others learn and build.