Problem Solver

Brandon Rodebaugh


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:

  1. Mathematical Modeling
  2. Statistical and Probability Analysis
  3. Theoretical Mathematics
  4. Scientific and Engineering Programming
  5. Algorithm Design and Analysis
  6. Evolutionary Algorithms
  7. Neural Networks
  8. Numerical Analysis
  9. Mathematical Optimization
  10. Materials Engineering

Brandon Rodebaugh's Problem Solving Experience:

  1. 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.
  2. 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.
  3. 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.