Professor at NC State, exploring how to make AI simpler, clearer, and more useful for software engineers.
My work connects software engineering, data-driven methods, and human insight.
I’ve helped build open research tools and ideas like PROMISE, EZR, and BINGO, used worldwide for teaching and experimentation.
Lately, I focus on minimal AI, active learning, and explainable optimization—how small, clear models can do more with less.
- Software Engineering & Empirical Methods
- Active Learning & Bayesian Optimization
- Minimal, Explainable AI
- Reproducible Research & Open Science
Designing lightweight methods for reasoning under uncertainty.
If a model can’t explain itself—or be rebuilt from a few lines of code—it’s not ready for engineers.
