My research studies long-term change processes using computational methods. I am interested in how complex systems change over time, why large-scale transformations occur, and what kinds of regularities can be identified across biological, ecological, and societal domains.
A central motivation of my work is that many systems of interest, including evolutionary systems, are historical, incomplete, and non-repeatable. This requires computational approaches that are robust to uncertainty and bias, and that support careful scientific reasoning rather than overconfident prediction.
My research spans three closely connected areas.
A core part of my work focuses on macroevolutionary and macroecological dynamics studied using fossil and other long-term data sources. I investigate large-scale evolutionary patterns such as diversification, expansion, persistence, and extinction, and how ecological relationships shape these processes over deep time.
This work contributes quantitative, data-driven perspectives to evolutionary biology while remaining attentive to the structure and limitations of the available evidence.
I develop computational and data-analytic methods for studying systems that evolve over time. Much of this work originates in computer science and data science, but is motivated by problems where change is intrinsic to the system being studied.
Topics include modelling evolving data, detecting and characterising change processes, long-term forecasting under uncertainty, and evaluating computational methods when assumptions of stability do not hold.
A further strand of my research examines AI ethics and the philosophy of AI, with a focus on the use of AI in scientific contexts. I study robustness, bias, and responsibility in machine-learning systems, and how computational tools influence scientific reasoning and interpretation.
This work is not limited to biology, but addresses general questions that arise when AI is used to support inference and decision-making in high-stakes settings.
Across these areas, my work aims to build conceptual and methodological bridges between evolutionary biology, computer science, and the study of complex systems. Rather than treating computational methods as neutral tools, I study how their assumptions interact with real-world data and with the scientific questions being asked.
A full list of publications is available on my Publications page and on Google Scholar.