- SUNY Chancellor’s Award for Excellence in Teaching (2003-2004)
- UAlbany Excellence in Teaching and Advising Award (2003-2004)
- Information Physics: Entropic Foundations of Quantum Mechanics, Statistical Mechanics and General Relativity,
- Entropic and Bayesian Inference; Information Geometry.
In recent years my research has focused on the connection between physics and information.
One goal has been to develop a general framework of entropic inference that allows one to tackle the central issues in a unified manner. The framework allows one to address questions that concern the nature of information and how it is to be processed: What is information? Or, from a Bayesian perspective, how is information related to the beliefs of rational agents? How does one update from prior probabilities to posterior probabilities when new information becomes available? Are Bayesian and maximum entropy methods compatible with each other?
The other goal has been to explore the extent to which the laws of physics might reflect the rules for processing information about nature. More specifically the objective is to derive statistical mechanics, quantum mechanics, and general relativity as applications of entropic inference. For statistical mechanics this goal was largely achieved in the work of E.T. Jaynes. My recent work has focused on the application to quantum theory. So far progress along this line of research has been reassuringly successful.
My papers on entropic inference and on its applications to the foundations of statistical mechanics and of quantum mechanics can be found here.