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While I believe I am very often capable of giving unique insight into methodologies and solutions to specific problems, the greatest contribution I or any researcher can provide is the ability to objectively analyze any data at hand, recognize inconsistencies or holes in the data, correct or adjust for the error, and provide a rigorous, objective, and verifiable solution. I will use my insight and pursue any intuition I may have during the problem solving stage, but will only present a rigorous solution.
Areas Brandon Rodebaugh is Knowledgeable in:
Applied Mathematics and Theoretical Computer Science, especially with applications to engineering and the sciences.
Techniques Brandon Rodebaugh Uses:
Use of statistical analysis to isolate accurate and usable data, mathematical modeling, optimization of models, introduction of error-checking into models, and, finally, validation of results with statistical tests.
While many individual problems can be solved with simple observation and by trial-and-error, the most useful solution will be as general and rigorous as feasible and objectively quantifiable.
Brandon Rodebaugh's Problem Solving Skills:
- Mathematical Modeling
- Statistical and Probability Analysis
- Theoretical Mathematics
- Scientific and Engineering Programming
- Algorithm Design and Analysis
- Evolutionary Algorithms
- Neural Networks
- Numerical Analysis
- Mathematical Optimization
- Materials Engineering
Brandon Rodebaugh's Problem Solving Experience:
- Created algorithms to model materials properties allowing for the extrapolation of engineering data from raw test results without the need of an experienced engineer. I developed these algorithms working independently.
- Developed mechanistic-empirical algorithms and a wrote computer program to model and design hot mix asphalt using California design requirements, minimizing the time needed for design by materials engineers. I developed this program working independently.
- Developed methods of training Neural Networks with multiple layers to be trained by back-propagation using evolutionary and particle swarm algorithms. I was a research associate working with a professor of computer science.