I have 25 years experience designing and implementing machine learning algorithms for data analysis applied to physical systems. I would characterize myself as a Computational Physicist, although I have done a great deal of both experimental and theoretical work.My areas of expertise include: Astrophysics, Bayesian Probability Theory, Information Theory, Robotics, Signal Processing, Neuroscience, Nonlinear Dynamics, Quantum Mechanics, Foundations of Physics and Earth Science.My overarching goal is to Develop the field of Information Physics- Understand how the Laws of Physics arise from Information- Understand biological information processing- Understand how Life arose to take advantage of information- Construction of truly intelligent machinesCurrently, I am working on three main projects:- Foundations of Physics It appears that some laws of physics can be derived from symmetries and order that constrain apt quantification by an observer-participant. Successes to date: -- derivation of quantum mechanics -- emergent space-time physics from quantification of causal sets -- derivation of properties of Fermions- Exoplanet Detection from Kepler data by reflected light- Intelligent Instruments that perform autonomous experimental designPast Projects Include:- Characterizing Organic Molecules in Space (funded by NASA AISRP)- Theory of Questions (working out the algebra and calculus of questions)- Estimate Entropy and Mutual Information from Earth science data (funded by NASA AIST)Specialties: Physics, Bayesian Probability Theory, Information Theory, Quantum Mechanics, Robotics, Machine Learning, Data Analysis, Astrophysics, Signal Processing, Neuroscience, Nonlinear Dynamics, Classroom Instruction, PhD Student Supervision and Direction, Grant Writing, Project Management, Research Lab Direction, Scientific Writing, Web Design, Database Design, Matlab, Java, C, XML, HTML, Javascript, SQL, PHP, FORTRAN, Forth