Research

My research focuses on developing AI systems that are both powerful and interpretable. I am particularly interested in bridging the gap between what can be used in a laboratory for benchmark, and what can actually be deployed in real-life settings.

Rule-Based Learning

I am deeply interested in rule-based learning systems that produce interpretable decision rules. Unlike black-box models, rule-based classifiers generate explicit IF-THEN rules that practitioners can examine, validate, and trust. I am explicitly interested in those that are particularly simple, and can be validated by humans.

My work in this area focuses on developing new applications for these methods and continuous optimization techniques for learning optimal rule bases.

Uncertainty Quantification with Incomplete Information

Real-world data is often incomplete, imprecise, or affected by the chaotic nature of real life processes. My research addresses how to make reliable decisions when working with such data. I am particularly interested in dynamic feature selection, and robust systems with low hardware resources.

Fuzzy Logic

Fuzzy logic provides the mathematical foundation for much of my work. It allows us to model imprecise concepts using degrees of membership rather than binary true/false values, making it particularly suited for human-centric AI applications.

I maintain Ex-Fuzzy, a Python library that makes fuzzy logic accessible to the broader machine learning community.

Collaboration

I am always open to collaborations on these topics. If you are interested in working together or have questions about my research, please feel free to reach out.