Introduction
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Current Research Interests
Artificial Intelligence, Biomedical Informatics, Semantic Web, and Data Visualization
Education
PhD in Health Informatics 2008-Present
Keck Fellow
The University of Texas Health Science Center
School of Health Informatics
MS in Electrical Engineering
The University of Memphis 2007
BS in Computer Engineering
The University of Memphis 2005
Summary
My research interest can be summarized as computation in biological and artificial systems. I am an evangelist of Complexity Science in health care and see its application as a way out of a deadly labyrinth. The United States currently is in the middle of a health care crisis with 45 million uninsured (and growing) while the increasing cost of care threatens to remove insurance from the insured. Furthermore, the system is plagued by error placing those who have access to the commodity in danger. A recent Institute of Medicine report found that preventable medical error is responsible for an estimated 100,000 deaths each year placing medical error related death higher than HIV and automobile accidents. I propose that error is an emergent property of a Complex Adaptive System whose behavioral states can be understood. I also forsee a myriad of applications for Complexity Science in health care including epidemiology, bioterroism defense, computational biology, and policy.
My previous and primary research as a graduate student at The University of Memphis was developing computational knowledge models for sensors and developing a framework leveraging the computational knowledge model to support dynamic discovery and synchronization of senor services to support high-level user constraints. The research at The University of Memphis was conducted under Dr. David J. Russomanno in the Department of Electrical and Computer Engineering. My long-term goal is to obtain a tenure track position at a research university. I find research a natural choice for my profession due to my drive for learning, yearning to solve problems, and desire to push the envelope of science.
Out September 2008
OntoSensor: An Ontology for Sensor Network Application Development, Deployment, and Management
By David J. Russomanno and J. Caleb Goodwin
This chapter describes the potential utilization of an ontology in heterogeneous sensor network environments to facilitate discovery, query, tasking, inference, and interoperation of a myriad of sensor types. As sensor networks advance, they will no longer be dedicated to specific applications in which a priori knowledge of the sensors’ capabilities and access methods are procedural bundled within application-specific software. In dynamic scenarios, declarative knowledge sources are required to support the on-the-fly utilization of sensors by software agents. A laboratory environment and evolving sensor ontology have been constructed to experiment with such scenarios. OntoSensor has been implemented using the Web Ontology Language to model sensor properties, associations, and services providing meta data about sensor types, as well as the knowledge required for subsequent interoperability given that the sensors may contribute to an agent’s overall goal.
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