I develop computational and conceptual foundations for understanding the changing world. Complex systems, whether biological, ecological, or societal, are in constant flux, and a central challenge of our time is learning to reason about them rigorously: identifying patterns of change, distinguishing signal from noise, and building computational methods that work when stability cannot be assumed.
I investigate macroevolutionary and macroecological dynamics using fossil and other long-term data, including expansion, persistence, and extinction at geological timescales, and how ecological relationships shape these processes. This work has produced findings on large-scale evolutionary patterns published in Nature, PNAS, and Nature Communications. A broader extension of this research, pursued through the HAT consortium which I lead, asks whether analogous patterns of change operate across other complex systems: ecological, cultural, linguistic, and societal. Do species, economies, languages, and cultures age in the same way? Can evolutionary intuitions developed over long timescales transfer to understanding rapid change in human systems?
I develop machine learning methods for evolving data, including change detection and adaptation strategies, and study their performance boundary conditions.
A growing line of my research is on how AI systems can support rigorous reasoning in science and other high-stakes contexts. When a machine learning model is used to produce a scientific measurement, support a consequential decision, or shape how evidence is interpreted, what formal conditions must it satisfy? I study learned measurement, robustness, fairness, and epistemic responsibility in AI systems, aiming to build the formal foundations that trustworthy AI use in reasoning requires.
The unifying question across the research themes is: how do we reason about the changing world?