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Washington and Lee University

Washington and Lee University
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Ericson Davis ('03)

Ericson is a Research Fellow at the Logistics Management Institute in McLean, VA.

After earning his B.S. in Mathematics in 2003, Ericson continued his mathematical studies at the University of North Carolina at Wilmington. Once he completed the requisite coursework for a Masters Degree, Ericson relocated to Washington D.C. and took a position conducting research in the area of image processing with CACI, a government consulting firm. Here, he found that a solid grasp of mathematics, especially probability and statistics, was imperative. While at CACI, Ericson developed algorithms for logo and signature detection/recognition using a machine-learning classification tool known as a support vector machine (SVM) trained with an integer programming optimization tool. In addition, he worked within a team to further the state of Arabic handwriting recognition. Centered around the use of Hidden Markov Models (HMMs) and other statistical techniques in Arabic handwriting recognition, this work was presented in a paper entitled "Combining different classification approaches to improve off-line Arabic handwritten word recognition" at the Document Recognition and Retrieval XV Conference in 2008.

 

In mid-2008, Ericson took the position of Research Fellow at the Logistics Management Institute, a government consulting organization specializing in defense logistics. With the math modeling group at LMI, Ericson has been able to use his knowledge of stochastic processes to enhance the simulations for modeling weapon system demand patterns. He has also found a welcoming new home for his expertise in probability and machine learning. A portion of his work involving simulation based optimization via integer programming, still in progress and entitled "Optimization and Analysis of Peak Demand Based Policies for Sporadic Demand Items" was presented at the 77th Military Operations Research Society Symposium in For Leavenworth, KS.

 

While at Washington and Lee, Ericson feels he learned "how to think" in his Real Analysis course. This grasp of logic and linear thinking, coupled with the applied mathematical techniques he later learned have been vital to his success. To current students, whatever their future plans may be, Ericson advises a healthy dose of mathematics, especially proof-based courses such as Real Analysis and Abstract Algebra. For those students interested in applied mathematics, he recommends at least 2 CS courses for a basic understanding of computer programming.