Research
Bayesian Networks, Multi-Belief Networks and Belief Polarization
Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations. They have seen widespread use in a number of fields including computer science, cognitive science, and philosophy. They offer a valuable tool to represent the complex inferential structure of beliefs. Notably, Bayesian networks provide a setting in which many rational belief polarization can naturally arise. The beliefs of agents move apart, even for ideally rational agents who update on precisely the same evidence. My research has focused on studying the phenomenon both between pairs of agents, and in a social setting, on networks of many agents.
Image credit: Martin Vorel
The Significance of Haag's Theorem in the Foundations of Quantum Field Theory
Haag's theorem appears to challenge standard techniques in quantum field theory, including the interaction picture and perturbation theory. My research, conducted with Marian Gilton and Chris Mitsch, investigates the implications of the theorem for foundational physics and the appropriate methodologies for addressing these challenges.
Bubble Chamber image from the Lawrence Berkeley National Laboratory
The Epistemology and Ethics of Artificial Intelligence
As AI systems become increasingly sophisticated, understanding how they function and how they should be deployed ethically becomes essential. My research emphasizes that understanding the epistemology of AI is a prerequisite for ethical reasoning about these technologies.
Image generated by OpenAI's ChatGPT using DALL·E 3 technology
The Epistemology of Models and Simulations in High Energy Physics
Computational models play crucial roles in high-energy physics research. My work focuses on "sloppy models"—those that depend on numerous parameters but are insensitive to most parameter combinations—and examines their relationship to scientific realism.
CERN Computer Centre (Cern/science Photo Library, 2010)
Reinforcement Learning and Evolution with Invention
I study bargaining games where agents can create novel strategies rather than simply selecting from predetermined options. My research investigates how unsuccessful strategies can diminish through evolutionary and reinforcement learning mechanisms.
Image credit: Jack Moreh
Vector Boson Pair Production at the ATLAS Detector
My prior particle physics work analyzed data from the Large Hadron Collider. This research measured WW and WZ boson pair production cross-sections and investigated anomalous triple gauge couplings. The work involved Monte Carlo simulations and analyses of both merged and separated decay products.
The eight toroid magnets of the ATLAS detector (Maximilien Brice)