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Research & Internships

Participating in research has many benefits for undergraduates, with both graduate school goals and industry career goals. Research problems challenge students' ingenuity and creativity, providing a challenge well beyond normal classroom study. Students often find that research introduces them to many new opportunities, often serving as a bridge to graduate school. Research also requires that students master underlying concepts. In research at Whitworth, students work closely with a professor under their leadership and in a mentoring relationship. The critical thinking and analytical problem solving skills developed while doing research provides students with extraordinary tools for a successful career or graduate school.

Students participating in grant research, are involved in original and notable, graduate caliber research, working closely under faculty leadership and as a research team. A team comradery develops, fostering creativity and accomplishment. As students prepare for graduate school, these students have the opportunity to submit papers on their research and to attend international conferences.

Research allows students to integrate knowledge from a variety of disciplines. Last summer two of our computer science students assisted physics professors with their research in air-glow phenomenon. Cross-disciplinary research broadens students' horizons and helps them gain an understanding of how different disciplines are connected. A primary project funded by the National Science Foundation, integrates computer science and biomedicine.

At Whitworth, motivated students are encouraged to participate in on-going research projects sponsored by departmental faculty.

Multi-Agents for Intelligent Monitoring Systems
Directed by: Dr. Susan Mabry
(Funded by National Science Foundation, 2 CISE-IIS Grants)

The IM-Agents research project focuses on intelligent multi-agents in a distributed environment with decision support mechanisms to assist patient health care.

The focus is upon patient monitoring and diagnostics, with collaboration with a regional Medical Center and physicians. Clinicians must simultaneously audit and interpret an overwhelming amount of information regarding a patient during the process of monitoring, performing diagnostics and determining interventions. Multiple IM-Agents coordinate as a team with each agent performing specialized monitor and diagnostic tasks.

For more information, please see the IM-Agents Project web page.

Simulation of a Magnetic Scoop Engine
The Magnetic Scoop program was designed to simulate a magnetic scoop engine in space. The goal of a magnetic scoop engine is to gather particles in space, and then project them out of the back of the engine, accelerating the engine through space using a minimal amount of on-board fuel. We managed to simulate the flow of electrons into a magnetic field, and then being accelerated out the back of the engine with an electric field. The goal of the program is to run various tests and develop the best combination of fields for an engine of this type.

Genetically Programmed Distributed Agents
This project represents a notable research project involving genetic programming and distributed artificial intelligence. Traditional approaches of genetic programming manipulate structured populations of entities in a static simulated environment with goals of deriving optimal solutions through mimicking biological processes of survival of the fittest almost always on a single processing platform. The DNA-MAS system is an agent system in which multiple, genetically programmed agents evolve in a distributed environment using a unique symbolic mutable language. Genetic programming is conducted upon a dynamic population of multi-agents that exist in and adapt to multiple changing environments, evolving the most fit agent symbolic code. In the DNA-MAS System, distinct design goals have been attained: a genetic programming symbolic language boasting instruction independence, operation code completeness and operation code balance; a self-organizing form of natural selection in multi-agents. Supported by a distributed, parallel processing approach, the system yields high performance in a computationally intense problem.