Identifying Key Selection Features as Predictors of Success for Independent Research and Development Projects

Deciding which independent research and development (IR&D;) projects to fund at a science and technology research center is a complex challenge. Given competing objectives, constrained resources, and technical maturity, research centers endeavor to select research projects that best align with mission priorities, have great impact, and ultimately lead to a maturity path for transition to government and industry. This praxis evaluates funded research projects that did and did not meet all their project objectives to determine if, during the proposal evaluation phase, we can predict in advance whether the research and development (R&D;) project will succeed. Correspondingly, machine learning-based predictive algorithms are employed to classify and identify key selection features as predictors for R&D; project success. A machine learning-based predictive model is then developed that can be used during the IR&D; project review, assessment, and selection process to predict in advance whether a proposed R&D; project will be successful, and consequently whether limited resources (e.g., funding, facilities, staff) should be allocated. This proposed solution offers a coherent and methodical selection formula and accurate predictive feature method for which future research projects can be evaluated, prioritized, selected and funded.

Author

• Hassien, Michael

Language

• en

Keyword

• predictive analytics
• machine learning
• research and development
• selection features

Date created

• 2020

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Etemadi, Amir
• Adetunji, Oluwatomi

Committee Member(s)

• Holzer, Thomas
• Adetunji, Oluwatomi
• Etemadi, Amir

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/76537210f