Applied Math

I can remember the first time math became really interesting to me.  It was a university class on Linear Programming.  It was then that I realized real-world problems that seemed too complicated to even begin contemplating, could be translated into math problems and solved.  Since then, my respect and love for applied math has grown tremendously.  I don’t claim to be a mathematician and I continue to struggle with the more advanced concepts.  However, because I understand its vast potential, I am more than willing to struggle with it until my brain aches and I finally get it.

I have taken bachelors and masters-level courses in the following areas:

  • Optimization
    • Linear Programming
    • Non-Linear Programming
    • Integer Programming
    • Goal Programming
  • Simulation
    • Monte Carlo Simulation
    • Discrete-event Simulation
  • Statistics
    • Factor Analysis
    • Principle Components Analysis
    • Multiple Discriminant Analysis
    • Cluster Analysis
    • Canonical Correlation Analysis
    • Forecasting (single time-series)
    • Linear and Non-Linear Regression
      • using Least Squares methods
      • using Maximum Likelihood methods
  • Calculus
    • simple differential
    • simple integral
My masters research also uses the following techniques:
  • Multi-Criteria Decision Making
    • Analytical Hierarchy Process
    • Analytical Network Process
  • Cluster Analysis
  • Association Rule Learning (a common data-mining method)


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