Algorithms like not only the design but also the analysis.

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 

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.


Exceptional research opportunities and excellent living conditions.

Selected Publications

Amin, T., & Moshkov, M. (2018). Totally optimal decision rules. Discrete Applied Mathematics, 236, 453–458. doi:10.1016/j.dam.2017.10.024
Chikalov, I., Hussain, S., & Moshkov, M. (2018). Bi-criteria optimization of decision trees with applications to data analysis. European Journal of Operational Research, 266(2), 689–701. doi:10.1016/j.ejor.2017.10.021
Azad, M., & Moshkov, M. (2017). Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions. European Journal of Operational Research, 263(3), 910–921. doi:10.1016/j.ejor.2017.06.026
Atminas, A., Lozin, V., & Moshkov, M. (2017). WQO is decidable for factorial languages. Information and Computation, 256, 321–333. doi:10.1016/j.ic.2017.08.001
AbouEisha, H., Chikalov, I., & Moshkov, M. (2016). Decision trees with minimum average depth for sorting eight elements. Discrete Applied Mathematics, 204, 203–207. doi:10.1016/j.dam.2015.10.030
Chikalov, I., Hussain, S., & Moshkov, M. (2016). Totally optimal decision trees for Boolean functions. Discrete Applied Mathematics, 215, 1–13. doi:10.1016/j.dam.2016.07.009
Busbait, M., & Moshkov, M. (2016). Diagnosis of three types of constant faults in read-once contact networks over finite bases. Theoretical Computer Science, 630, 26–42. doi:10.1016/j.tcs.2016.03.023