Research

Research

I work on two main problems. The first is consciousness: what it is, physically, and how the mathematics of dynamical systems gives us a real answer. The second is perioperative data science: using machine learning, NLP, and large-scale analytics to make surgery safer.

These programs are connected. Computational neuroscience and LLM interpretability work sits between them, using language models as testbeds for predictions that come out of the consciousness theory. The same geometric and topological ideas appear in both programs.

Main Programs

Consciousness Science (State Space Theory) The State Space Theory of consciousness, its metaphysical framework (Computational Dynamic Monism), and the mathematical foundations that make it precise. This is my primary theoretical research program. Published in Journal of Consciousness Studies (2025), with the unification paper in Nonlinear Dynamics, Psychology, and Life Sciences (2026).

Perioperative Data Science Machine learning, NLP, and multicenter analytics for surgical and anesthetic outcomes. LLM evaluation for clinical risk prediction. Cardiac arrest prediction. National quality benchmarking through MPOG. Published in JAMA Surgery, Circulation, and others.

Connecting Work

Computational Neuroscience & LLM Interpretability Using large language models as computational analogs to test SST’s predictions about representational geometry, binding, and topological structure. The dreaming experiments. The intervention window finding. Function space topology across architectures.

Ongoing Clinical Research

Pediatric Perioperative Quality Quality improvement and outcomes research for children undergoing surgery. I chair the MPOG ASPIRE Pediatric Quality Committee. ERAS protocols, sugammadex, hypothermia prevention.

Mobile Health & Clinical Decision Support The Anesthesiologist app (500,000+ downloads) and other clinical tools. A clinical tool and research platform used in nearly every country.