Mikhail Moshkov is Professor of Applied Mathematics and Computational Science and Principal Investigator of the Extensions of Dynamic Programming, Machine Learning, Discrete Optimization Research Group (TREE).

Education and early career

Moshkov holds an M.S., Diploma Summa cum Laude, State University of Nizhni Novgorod, Russia, 1977, and obtained a Ph.D. and a Doctor of Science Diploma from the University of Saratov and Moscow State University, respectively. Before KAUST, he held a professorship at the University of Silesia, in Poland.

Areas of expertise and current scientific interests

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 the study of relationships between two cost functions with applications to combinatorial optimization and data mining.

Career recognitions

Moshkov is the recipient of the First Degree Research Prize, October 2006, awarded by Rector of the University of Silesia, Poland, and of the State Scientific Stipend in Mathematics for Outstanding Scientists, April 2000 – March 2003, awarded by the Presidium of Russian Academy of Sciences.

Editorial activities

Professor Moshkov is the author of more than 140 publications and books such as 

• Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions
• Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining 
• Three Approaches to Data Analysis: Test Theory, Rough Sets and Logical...
• Combinatorial Machine Learning: A Rough Set Approach 
• Inhibitory Rules in Data Analysis: A Rough Set Approach
• Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications.

Editorial Memberships

• 2003-present, Member of the Editorial Board, “LNCS Transactions on Rough Sets”, Springer.

Why data analysis?

Data analysis is one of the main challenges of the current century.

Why KAUST?

Exceptional research opportunities and excellent living conditions.

Dissemination

• Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining 
• Three Approaches to Data Analysis: Test Theory, Rough Sets and Logical...
• Combinatorial Machine Learning: A Rough Set Approach 
• Inhibitory Rules in Data Analysis: A Rough Set Approach 
• Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications

Education Profile

• ​D.Sc. Moscow State University, Russia, 1999
• Ph.D. Saratov State University, Russia, 1983
• M.S. Diploma Summa cum Laude, State University of Nizhni Novgorod, Russia, 1977