My research aims at accounting for the success of scientific techniques and practises that are at odds with facts about the world, our best scientific theories, and common sense. It also aims at building bridges between philosophy and science.

I have two primary research strands. One strand focuses on foundational and conceptual problems that concern statistical mechanics and its relationship with thermodynamics. The other focuses on the success of modelling practises within science.

My research on the foundational and conceptual problems of statistical mechanics and thermodynamics is currently aimed at addressing questions like: Why do isolated systems away from equilibrium spontaneously approach equilibrium? Why do they remain in equilibrium for incredibly long periods of time?  Why do temporally-asymmetric equations, such as the Langevin equation, the Fokker-Plank equation, and the Boltzmann equation, accurately describe the behaviour of systems whose underlying dynamics are symmetric under time-reversal? What is the status and scope of the relationship between Gibbsian and Boltzmannian statistical mechanics? If the world is deterministic, then why is statistical mechanics predictively accurate? What are statistical mechanical probabilities? And, how exactly is thermodynamics connected to statistical mechanics? I am also currently trying to resolve what is known as the measure zero problem, which, in a nutshell, involves justifying why we are entitled to neglect sets of measure zero in the application of certain theorems relevant to statistical physics. 

My research on scientific modelling is currently aimed at identifying the roles and at explaining the success of non-representational models within science. I am addressing questions like: How do scientists use these models? Why can they be used to help construct successful theories about the world and to generate helpful hypotheses about actual phenomena when they do not represent real world systems? How are these models related to other scientific models? Under what conditions do models represent features of the world? And, under what conditions do they do so accurately? 

I am also interested in the philosophical foundations of probability theory, Bayesian approaches to confirmation, decision theory, and climate science.