Charles E. Davidson, Ph.D.

Copyright © 2003
Charles E. Davidson
All rights reserved.

Welcome to Charlie Davidson's Web page! I received my doctorate from Clarkson University in analytical chemistry in December of 2003. While I have solid skills in standard analytical instrumentation, my interests lie more towards data analysis than data collection. Therefore, perhaps I can better introduce myself by saying:

This is certainly more specific than analytical chemist. However, chemometrician is not a well known term, and the standard short answer to "What is chemometrics?" (namely, "a combination of chemistry and statistics") is an over-simplification. Although true, it is, I believe, limiting. As illustrated by my research, a chemometrician's skills enable him to go beyond the borders of this definition.

It is true that I develop and frequently apply multivariate data analysis techniques to chemical data, such as spectroscopic and chromatographic data sets. And I frequently compute means, standard deviations, and the like. But I have also mined QSAR (Quantitative Structure-Activity Relationship) data, and investigated biological data sets (such as genomic and proteomic data) and even some physical data. While chemical data have attributes that allow chemometric methods to succeed, those attributes are shared by many other data types.

My work also involves a significant amount of computer programming. I have written routines for Principal Components Analysis (PCA), Partial Least Squares (PLS), Multidimensional Scaling (MDS), and Multivariate Curve Resolution (MCR). Some routines I have written venture into the field of artificial intelligence, such as Neural Networks (NNs) and Genetic Algorithms (GAs). Indeed, a good portion of my work has involved integrating all of the above with a GA.

Focusing on the area of data analysis is inherently multidisciplinary, since a knowledge of chemistry and statistics is necessary for successful analysis. My work with GAs has further convinced me of the broad scope of chemometrics. The GA has served as a toolbox that performs successfully on many types of data, and solves many types of problems. Therefore, I believe that to study chemometrics is also to study chemistry, chemoinformatics, bioinformatics, computer science, artificial intelligence, and even philosophy. It is certainly my firm belief that all of these fields connect, and in such a way that the distinctions betwen them become blurred.

If this short introduction intrigues you, please explore this site. Links to my resume are available on the left hand side of this page (go there now). In the resume you will find my contact information, qualifications, publication and presentation lists, and references. Questions or requests for more details can be sent to me, or to the people listed as references. Thank you!

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