Microarray technology provides a way of studying which genes are active in different types of cell tissue, but analyzing the resulting data has many challenges. Unlike traditional statistics in which one has many more replicates than variables, in microarray data there are many more variables (the genes) than there are replicates (the chips or slides), so new statistical methods, or at least ways of pre-processing the data so that traditional means can be used, are needed. I am currently working on useful methods of preprocessing. This need to find ways of analyzing &&&small samples&&& (i.e., few replicates) also occurs in studies at the College of Veterinary Medicine, where I analyze data from experiments, often with small samples. So my work in each area compliments the other.

My other and original research hat is in the fields of Algebraic K-Theory and cyclic and Hochschild homologies, where my research has centered on determining the relationships between the K-theory and the homology theories and exploiting these relationships to provide algorthms for computing the K-theory and cyclic homology.