Dr. Irina G. Malkina-Pykh is a leading researcher at the Department of Human Ecology and Head of Laboratory of Health Psychology of the Research Center for Interdisciplinary Environmental Cooperation of Russian Academy of Sciences (INENCO RAS), Saint-Petersburg, Russia. Irina studied at Moscow State University, Moscow Physical and Technical Institute and International Academy of Psychology, Business and Management, St. – Petersburg, Russia. Her academic background is in biophysics and her theses (PhD and Dr.Sci.) were in method of response functions. Irina is a world-famous leading expert in the field of mathematical modelling of complex ecological and social systems. Also, she is the author of the theory and method of rhythmic movement therapy – treatment approach that integrates the methods of Body-oriented psychotherapy, Dance movement psychotherapy and rhythmic gymnastics (aerobics). Irina is a full member and Professor of Sport Psychology at Baltic Academy of Education, St-Petersburg, Russia where she is teaching Psychosomatics and Rhythmic Movement Therapy courses. She is teaching undergraduate, graduate and post graduate level classes in Systems Analysis in Psychology and Sociology at Cognitive and General Psychology Department of Astrakhan State University, Astrakhan, Russia. She is a Fellow of the Wessex Institute of Technology (WIT), UK. Irina has published on mathematical models in psychology and sociology and rhythmic movement therapy in Journal of Health Psychology, Spanish Journal of Psychology, Ecological Modelling, Ecological Indicators, International Journal of Body, Movement and Dance in Psychotherapy, WIT Press transactions, journals and books.
Mathematical psychology and sociology, psychosomatic researches, health psychology, method of response function. She is researching the effectiveness of rhythmic-movement therapy interventions for disordered eating behaviours and obesity, subjective well-being, alexithymia, etc. Also, she is researching the application of generalized multiplicative nonlinear models based on the method of response functions for evaluating outcomes in psychotherapy.