He earned his Doctoral degree in Quantitative Psychology from the University of Illinois, Champaign, Illinois. His research concerns applications of combinatorial optimization methods to scaling/unfolding, clustering/tree-fitting, and order-constrained matrix decomposition problems, with particular focus on the analysis of individual differences based on sets of multiple proximity matrices, as might be collected from different data sources in the context of cross-sectional or longitudinal studies. He has also worked on algorithms for the p-median clustering of large data sets and the clique partitioning problem.