Sean Clayton SeanClay10

Sean Clayton

Welcome to my GitHub!

Academics: Senior Computer Science student at Oregon State University, concentrating on ethical AI implementation, machine learning, and software architecture.
Technical Interests: AI, RAG systems, DevOps, and ML.
Professional Goals: Build impactful, privacy conscious software and AI tools that solve real world problems.
Hobbies: Soccer, hiking, and playing guitar.

Description: A pipeline written in Python that leverages XGBoost and LLMs to extract predator-prey interaction data from a global database of diet surveys, enabling the validation of the fraction of feeding predators.
Key Features:
- Preprocesses ecological text data and applies TF-IDF vectorization for feature extraction.
- Uses XGBoost to classify relevant publications with key predator diet metrics.
- Utilizes local LLMs for deeper extraction and analysis of complex unstructured text.
Technologies Used:
- Python
- XGBoost
- sk-learn

Description: An autonomous AI agent that plays Pokémon Red using computer vision and a locally hosted Vision-Language Model, eliminating API costs while maintaining complex decision-making capabilities.
Key Features:
- Achieves autonomous gameplay through real-time screenshot analysis and strategic decision-making using Qwen3-VL.
- Reduces inference costs from ~$100 per playthrough to $0 by running entirely on local hardware with 4-bit quantization.
- Hybrid perception system combining computer vision, RAM memory hooking, and collision map generation for robust spatial reasoning.
Technologies Used:
- Python
- Qwen3-VL
- PyBoy
- HuggingFace Transformers

Description: A local, privacy focused RAG system that enables deep analysis of PDF documents using knowledge graphs and local LLMs.
Key Features:
- PDF upload and interactive query interface via Streamlit.
- Knowledge graph construction from document concepts and relationships.
- LLM-powered reasoning with Ollama for context-aware answers, fully local ensuring privacy.
Technologies Used:
- Streamlit
- LangChain
- Docker

Description: A performance optimized poker simulator written in C++ that implements advanced data structures and algorithmic techniques to simulate realistic poker gameplay.
Key Features:
- Multi-threaded functionality for enhanced performance.
- Realistic game mechanics, including betting rounds, hand evaluations, and decision-making logic.
- Scalable design allowing for multiple players and various poker variants.
Technologies Used:
- C++
- Qt
- CMake

Languages: C, C++, Python, JavaScript
Tools & Frameworks: LangChain, React, Node.js, sk-learn, Docker, Kubernetes
Methodologies: Agile Development, Test-Driven Development (TDD), Scrum Methodology

I'm always open to collaborating on exciting projects or discussing ideas. Feel free to reach out: