PREM K. GOEL

Professor; Ph.D., Carnegie Mellon, 1971.
Home Page: http://www.stat.osu.edu/~goel

My research interests include theoretical and practical aspects of Bayesian hierarchical modeling for nonlinear dynamic systems, longitudinal studies, Bayesian networks, information theory and comparison of experiments, and combining information and data fusion. Currently, my effort is focused on (i) statistical information theory, in collaboration with Professor Ginebra, Polytechnic University of Barcelona, (ii) statistical image processing, pattern recognition and transportation networks modeling and estimation, in collaboration with faculty in Transportation Engineering and Remote Sensing, (iii) Bayesian estimation for non-linear dynamic systems and data-rectification with application in process engineering, in collaboration with Professor Bakshi, Chemical Engineering and (iv) Modeling of Bone Mass Accretion in Young Females based on a 7-years longitudinal study, in collaboration with Dr. Matkovic, Physical Medicine. Statistical information theory studies include foundational issues in characterizing information measures in decision analysis framework. The Transportation engineering applications include developing space-time models for combining information on traffic counts from remote-sensed satellite and airborne sensors and ground data. Remote-sensed data provides spatial images at an instant in time, while the ground-based Automatic Traffic Recorder provides short-term, temporal data at a few fixed locations. The goal is to reduce costs and/or errors in estimating transportation planning parameters. The study also involves image processing for automatic pattern recognition of vehicles in airborne and high-resolution satellite images. Current research funding include, grants from DoT/BTS, and NIH and DOT funded, Ohio State led consortium for Application of Remote Sensing in Transportation Flows.