My primary interests are in philosophy of science, epistemology, and philosophy of information. I also have spent time working on issues in machine learning, digital humanities, metaphysics, and philosophy of physics. My dissertation is on the role that interventions play in the causal modeling framework for scientific explanations, causal inferences, and modal semantics.
Works in Progress
- How to Account for Counterfactuals While Respecting the Laws
In this paper I argue that in a deterministic world Lewis-style "small miracles" make the evaluation of counterlegals impossible.
- Ontological Levels and Abstract Objects
In this paper I make a case for taking the existence of abstract objects more seriously and argue that the 'levels' metaphor for discussing objecthood and fundamentality misleads us from a proper understanding of abstract objects.
- Toulmin and the D-N Model of Explanation
In this paper I explore the work by Stephen Toulmin as it relates to the Deductive-Nomological model of explanation and develop a Toulmin-style isomorph to that model.
In addition to working on explanation and the foundations of graphical models I enjoy learning the tools and techniques of scientists who work with those models. To that end I've been working on some projects that use the data produced by professional philosophy to get a network-level view of the field. See below for the results of this work.
Before graduate school I worked with the Statistical Relational AI Team at Wake Forest University Baptist Medical Center to develop an AI pipeline for Alzheimer's diagnosis using statistical relational learning and expert knowledge. This work was presented at the 2012 Internation Conference on Machine Learning and Applications (ICMLA) [.pdf] and later published in the International Journal of Machine Learning and Cybernetics (IJMLC) [.pdf].