Welcome to my GitHub! I am an AI Architect and Engineering Lead with expertise in advancing AI and ML initiatives, particularly within the federal sector. My work bridges the gap between academic research and practical applications, focusing on:
- AI Engineering: Designing scalable AI systems that meet enterprise and mission-critical needs.
- MLOps: Building robust pipelines to support model lifecycle management and operational excellence.
- Data Governance: Ensuring data quality, security, and compliance across large-scale systems.
- AI Capability Development: Advancing AI maturity modeling and implementing next-generation NLP and LLM solutions.
Currently, I lead the development of cutting-edge AI capabilities for the Summit Data Platform at the U.S. Department of Veterans Affairs, empowering innovative solutions for real-world challenges.
- AI Capability Maturity: Developing frameworks to assess and enhance AI maturity, focusing on MLOps, CloudOps, and data governance.
- Advanced LLM Applications: Exploring large language models (LLMs) for retrieval-augmented generation (RAG), function calling, and responsible AI practices.
- Psychology and AI: Integrating psychological trait models with AI to advance applications in education, healthcare, and personalized learning environments.
- Curates and prioritizes AI use cases for enterprise-wide implementation.
- Develops AI maturity stages with a focus on enhancing DevOps, DataOps, and AI Ops layers.
- Processes over 1.5 million clinical notes daily to provide actionable insights into social determinants of health.
- Leverages generative AI to support veteran-focused applications, enabling scalable NLP solutions.
- A customizable generative AI template integrating Azure OpenAI with large language models (LLMs).
- Employs Retrieval-Augmented Generation (RAG) to enable "chat with your own data" capabilities without requiring model fine-tuning.
- Key features include:
- Azure AI Search: Simplifies data ingestion, transformation, indexing, and multilingual translation for seamless retrieval.
- Dynamic Prompts: Adapts based on model type for optimal performance and allows personalized interactions through customizable settings.
- Explainability and Citations: Delivers traceable responses with references for verification.
- An end-to-end reference solution, complete with documentation and deployable code, to support the creation of domain-specific AI systems.
- Builds knowledge graphs based on NIST SP 800-53 to visualize control relationships and analyze compliance structures.
- Integrates cybersecurity governance, risk, and compliance (GRC) with AI risk management for enhanced oversight.
I teach Advanced Applications of Large Language Models (LLMs), a graduate course that dives into foundational AI models, prompt engineering, ethical considerations, and hands-on projects. The course covers:
- Foundations of LLMs and transformers
- Prompt engineering and RAG
- Practical applications with LangChain, LlamaIndex, and graph data structures with LangGraph AA-LLM-Course
- Expanding NLP Pipelines: Enhancing NLP pipelines on the Summit Data Platform (SDP) with generative AI and LLM support.
- Implementing the Public Sector Information Assistant: VA teams are able to interact with their own data.
- AI in Compliance: Applying AI to compliance data, including knowledge graphs for NIST 800-53 controls and DASF 2.0.
- Data Governance for AI: Developing a comprehensive AI/ML metamodel to support traceability, security, and governance.
- Exploring Personality Models: Creating a comprehensive character model based on multiple personality theories.
- Exploring Neo4j Graph Visualizations: Visualizing personality models and AI maturity models with connected data insights.
Feel free to reach out for collaborations, discussions on AI applications, or general questions:
Thank you for visiting my page!