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Research & Internships
The Whitworth Math & Computer Science Department has an exceptional record in undergraduate research and internships. Motivated students are encouraged to participate in research projects sponsored by departmental faculty. Research and internships introduce students to new opportunities, often serving as a bridge to graduate school and/or industry employment. Research problems challenge students' ingenuity and creativity, providing a challenge well beyond the normal classroom experience. Students participate in original and notable, graduate caliber research, working closely with a professor and often with other students under faculty leadership and in a mentoring relationship. A sense of comradery often develops, fostering creativity and accomplishment. Critical thinking and analytical problem solving skills developed, provides students with extraordinary tools for a successful career or graduate school. The research program begins with the Research Methods course. There are opportunities for fellowships and paid appointments as research assistants. As students prepare for graduate school, these students have the opportunity to submit papers on their research and to attend conferences. Cross-disciplinary research broadens students' horizons and helps them gain an understanding of how different disciplines are connected. A number of our students also participate in National Science Foundation summer REUs at other universities. A number of Whitworth projects span across math and computer science.
Internships provide valuable experience, allowing students to merge their education with practical application, while learning to function in the world of industry. Internships also allow students to explore different types of companies and employment environments, as they seek their own preferences. Internship experiences frequently pave the way for permanent employment following graduation. Whitworth’s MACS program has a strong record of internships with placements such as: Microsoft Corporation, Boeing Corporation, Adobe Corporation, NextIT Corporation, Pacific Northwest National Laboratories, and numerous others.
At Whitworth, motivated students are encouraged to participate in on-going research projects sponsored by departmental faculty.
Spectrum-Agents Decision Support for Autism Spectrum Disorders
Directed by Susan Mabry, Ph.D.
Collaborators: Betty Fry Williams, Ph.D., regional autism network including specialists from the Northwest Autism Center and the Spokane Guilds School & Neuromuscular Center
Current research thrust is Spectrum-Agents, a system designed specifically to support Autism Spectrum Disorder clinicians and caregivers. The overarching vision of the project is a highly specialized clinical decision support system with a seamless suite of tools revolving around the interest of early identification, comprehensive evaluation, patient monitoring and evaluation of effective intervention programs. The system is highly scaleable, employing federated agents and applied artificial intelligence methods of fuzzy logic, belief networks and machine learning. For information, visit http://news.whitworth.edu/2009/07/whitworth-professors-students-develop.html
Multi-Agents for Intelligent Monitoring Systems
Directed by Susan Mabry, Ph.D.
(Funded by National Science Foundation, 2 CISE-IIS Grants)
The IM-Agents research project focused on intelligent multi-agents in a distributed environment with decision support mechanisms to assist trauma care and critical care of patients. The focus was on patient monitoring and diagnostics, in 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.
Revising Sleep Models
Directed by: Michael Rempe, Ph.D.
Rempe is an applied mathemitician interested in applying math to biological systems, particularly neuroscience. He develops mathematical models to simulate the function of individual cells or brain regions. Rempe recently published a model of sleep in the human brain under normal conditions and during the sleep disorder called narcolepsy (Rempe M.J., Best J., Terman, D.J. Math Biol. May 2010). Rempe is currently working on revising his model of sleep to simulate other sleep maladies, including insomnia.The goal in developing such models is to suggest possible mechanisms that neural systems employ both in normal function and in diseased states. These hypotheses can then be tested by an experimentalist to gain further insight into potential treatments.
The mathematical models Rempe develops typically consist of systems of ordinary or partial differential equations.To analyze the behavior of the models, Rempe uses mathematical tools, like phase-plane analysis, as well as numerical methods.
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 includes 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.