I'm fascinated by the challenge of making AI systems practical, reliable, and useful. I love building full-stack systems that bridge the gap between a cool concept and a dependable, real-world tool.
My work generally focuses on three areas:
Scalable Systems: Engineering the resilient, fault-tolerant data foundations that AI needs to function at scale.
Language Interpretation: Moving beyond static knowledge to build agents that can actively source and reason over live, complex data.
Autonomous Action: Exploring how to get multi-agent systems to plan, collaborate, and recover from failure to solve complex problems.
When I'm not focused on that, I love exploring the creative side of code through art, games, and the interaction between art and technology.
- Supervised Machine Learning: Regression and Classification — Deeplearning.ai & Stanford Online
- Data Engineering Specialization — Deeplearning.ai & AWS
