My research is highly interdisciplinary, spanning linguistics, mathematics, philosophy, and computer science. My domains of interest are in mathematical linguistics, theoretical deep learning, neuro-symbolic artificial intielligence, and philosophy of science. My research focuses on developing mathematical frameworks to study intelligence across natural, artificial, and collective systems.

Intelligence manifests in diverse forms, from neural networks and slime molds to social groups and AI systems, yet research across neuroscience, computer science, biology, and related fields often proceeds in isolation. A major barrier to progress is the absence of shared conceptual and mathematical tools that can connect across these domains. I work to establish formal frameworks that treat intelligence as a structural and functional property of systems, rather than a uniquely human trait. This requires moving beyond anthropocentric assumptions to examine the underlying capacities that emerge across different substrates and organizational scales: perception; learning; memory; reasoning; adaptation.

Some core questions of interest include:

• What regularities exist across different forms of intelligence?
• How do systems represent and generalize information?
• What principles govern learning and adaptation at different scales?
• How can we characterize and predict the behavior of intelligent systems across substrates?

The goal is to identify common principles that operate across scales and systems, especially in the domain of meaning, reasoning, and abstraction.