# Authors and Contributors

## Project Creator

**Joaquín Stürtz** (Manifold Laboratory) is the primary creator and architect of Manifold and Geodesic Flow Networks. The laboratory focuses on advancing the field of geometric intelligence through the application of physical principles to machine learning architectures.

For inquiries about the project, please contact Joaquín Stürtz through the official GitHub repository.

## Core Team

### Project Lead

**Joaquín Stürtz** is the project lead, responsible for the overall direction, architecture decisions, and quality standards of the Manifold project. This role encompasses technical leadership, community management, and strategic planning for the project's development roadmap.

### Core Contributors

Core contributors are individuals who have made significant and sustained contributions to the Manifold project. These contributions may include major feature implementations, substantial code refactoring, comprehensive documentation, benchmark development, or other contributions that have meaningfully advanced the project.

Core contributors have write access to the repository and participate in code reviews and technical discussions. They are expected to maintain the project's quality standards and help guide the direction of development.

## Contributing Community

Manifold benefits from a vibrant community of contributors who help improve the project through various means:

### Code Contributors

Developers who contribute code to the project, including bug fixes, new features, performance optimizations, and code refactoring. All code contributors are acknowledged in the project's commit history and, with their permission, listed in this file.

### Documentation Contributors

Writers and technical communicators who improve the project's documentation, including API documentation, tutorials, guides, and examples. Documentation is crucial for user adoption and is highly valued by the project.

### Issue Reporters

Researchers and users who report bugs, suggest improvements, and provide feedback on the project's functionality. Issue reports help identify problems and guide the prioritization of development efforts.

### Community Supporters

Members of the community who help other users through discussions, answering questions, and providing guidance on using Manifold effectively. Community support is essential for creating a welcoming and productive environment for all users.

## Recognition Framework

### Contribution Types

Manifold recognizes contributions across several dimensions:

**Code Contributions**: Implementation of features, bug fixes, performance improvements, and code refactoring. Major code contributions are recognized in release notes and may be highlighted in project communications.

**Documentation**: Writing and improving documentation including API references, tutorials, guides, and examples. Documentation contributions are acknowledged and considered essential to the project's success.

**Testing**: Developing and maintaining test coverage, including unit tests, integration tests, and benchmark evaluations. Testing contributions help ensure code quality and prevent regressions.

**Research**: Contributing to the theoretical foundations of geometric sequence modeling, including mathematical derivations, benchmark design, and experimental evaluation.

**Community**: Supporting other users, answering questions, and contributing to discussions in GitHub issues and other forums.

### Attribution

Contributors are attributed in the following ways:

- **Commit History**: All contributions are recorded in the Git commit history, preserving the authorship of each change.
- **Release Notes**: Significant contributions are highlighted in release notes accompanying new versions.
- **Documentation**: Major contributors may be mentioned in relevant documentation sections.
- **Communication**: With permission, contributors may be featured in blog posts, presentations, and other communications from the Manifold Laboratory.

## Contributing to Manifold

We welcome contributions from the community in all forms. Whether you are fixing a typo in the documentation, reporting a bug, implementing a new feature, or improving the mathematical foundations, your contributions are valued.

For information about how to contribute, please see the [CONTRIBUTING.md](CONTRIBUTING.md) file. This guide provides detailed instructions for setting up your development environment, following coding standards, writing tests, and submitting pull requests.

## Open Source Recognition

Manifold is published under the Apache License 2.0, an open source license that permits free use, modification, and distribution of the software. We are grateful to the open source community for the tools, libraries, and knowledge that have made this project possible.

### Dependencies and Inspirations

Manifold builds upon several important open source projects and research works:

- **PyTorch**: The deep learning framework that powers Manifold's tensor computations and automatic differentiation capabilities.
- **NumPy**: Scientific computing foundation for numerical operations and array manipulation.
- **Scientific Literature**: The project draws inspiration from research in geometric mechanics, Riemannian geometry, and state space models for sequence modeling.

## Contact Information

For questions about Manifold, contributions, or collaboration opportunities, please:

- Open an issue on GitHub for bug reports, feature requests, or questions
- Review the documentation at docs/ for guidance on using Manifold
- Check existing issues before opening new ones to avoid duplication

## Acknowledgments

We thank all contributors who have dedicated their time and expertise to making Manifold a successful project. Your contributions advance the field of geometric sequence modeling and make powerful tools accessible to researchers and practitioners worldwide.

The journey of Manifold is one of collaboration, learning, and discovery. We are committed to fostering an inclusive and supportive community where all contributors can grow and succeed.

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*This document recognizes the collaborative nature of open source development and the value of every contribution to the Manifold project.*
