I am an Omidyar Postdoctoral Fellow at the Santa Fe Institute. My main areas of research are philosophy of science and formal epistemology. Broadly speaking, I am interested in using formal tools from machine learning and artificial intelligence to generate new insights in epistemology and philosophy of science. I also work on the epistemology of astrobiology, the relationship between risk and ignorance, and Bayesian confirmation theory. You can listen to me discuss several of my research projects on the Santa Fe Institute's Complexity Podcast.
I did my PhD in the Department of Philosophy, Logic and Scientific Method at the London School of Economics. My dissertation was about causal explanation, and specifically on issues concerning granularity of explanation, variable choice, and inter-theoretic relations between the various sciences. In 2014, I earned an MSc in Philosophy and Public Policy, also from LSE.
In addition to my research, I have taught as a Graduate Teaching Assistant at both LSE and King's College London. My teaching experience includes courses in Logic, Philosophy and Public Policy, Political Philosophy, Business Ethics, and Philosophy of Science.
Causal Feature Learning for Utility-Maximizing Agents (with David Watson). Proceedings of the Tenth International Conference on Probabilistic Graphical Models (PGM).
- 2019On the Explanatory Depth and Pragmatic Value of Coarse-Grained, Probabilistic, Causal Explanations. Philosophy of Science. 86(1): 145-167.Inductive Explanation and Garber-Style Solutions to the Problem of Old Evidence. Synthese. 196(10): 3995-4009. Awarded the 2017/18 Popper Prize for Work by an LSE PhD Student.
Imprecise Bayesian Networks as Causal Models. Information. 9(9), 211. (Special Issue on Probabilistic Causal Modelling in Intelligent Systems)
The Problem of Granularity for Scientific Explanation. PhD awarded November 30, 2019.
- The Guardian April 5 2020.