Professor Irina Perfilieva, Ph.D., received the degrees of M.S. (1975) and Ph.D(1980) in Applied Mathematics from the Lomonosov State University in Moscow, Russia. She is Professor Honoris Causa in Amity Institute of Information Technology, Noida, India, and Doctor Honoris Causa in the University of Latvia, Latvia. At present, she is full professor of Applied Mathematics in the University of Ostrava, Czech Republic.

She is the author and co-author of six books on mathematical principles of fuzzy sets and fuzzy logic, the co-editor of one book and many special issues of scientific journals. She has published over 270 papers in the area of multi-valued logic, fuzzy logic, fuzzy approximation and fuzzy relation equations. 

She is an area editor of Soft Computing, member of editorial boards of the following journals: Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems, Journal of Computational Intelligence Research, Iranian Journal of Fuzzy Systems. She works as a member of Program Committees of the most prestigious International Conferences and Congresses in the area of fuzzy and knowledge-based systems.

For her many years of scientific achievements, she has received several awards, including an IFSA fellow and an honorary member of EUSFLAT. She received the 1st memorial Da Ruan award in 2012. She has two patents in the area of time series processing and the Internet service technique.

Her research interests are in the areas of fuzzy logic, mathematical modeling based on rigorous functional analysis, image processing, and complex numerical methods. She has recently published several papers on the mathematics of machine learning and neural network computations.

She is the author of the generally accepted method of fuzzy transforms, successfully used in image and time series processing, numerical analysis and methods to solve differential and differential-integral equations, including fractional ones and those over fuzzy-valued functions. Her recent interests are in the area of data analysis and the mathematical foundation of neural networks. She successfully uses modern as well as classical approaches.

According to ScholarGPS analytics, she is a highly ranked lifetime scholar, ranking 9th in the field of fuzzy logic.