Professor Moshkov's research interests include: Study of time complexity of algorithms in such computational models as decision trees, decision rule systems and acyclic programs with applications to combinatorial optimization, fault diagnosis, pattern recognition, machine learning, data mining and analysis of Bayesian networks. Analysis and design of classifiers based on decision trees, reducts, decision rule systems, inhibitory rule systems, and lazy learning algorithms. Extensions of dynamic programming for sequential optimization relative to different cost functions and for study of relationships between two cost functions with applications to combinatorial optimization and data mining.

Applied Mathematics and Computational Science, Computer Science, Machine Learning