Andrey Yuryevich Molchanov

Andrey Yuryevich Molchanov is one of the leading Russian experts in instrumental psychophysiological lie detection, serving as a tutor at the Russian Presidential Academy of National Economy and Public Administration (RANEPA) and the University of the Ministry of Internal Affairs of Russia. He is the author of original techniques of polygraph tests and training courses, and serves as school principal at the International Academy of Lie Research. He co-authored the textbook-monograph "Instrumental lie detection (academic course)" with S. I. Ogloblin, and his earlier work includes the "Atlas Poligramm" (2007), which contains over 300 illustrations of recordings of polygraph reactions gathered from authentic cases.

Molchanov's research contributions to polygraph science focus on applying machine learning and neural networks to improve test accuracy and automation. His 2022 study proposed a machine learning approach to automate the polygraph examiner's work using neural network architectures from the scikit-learn library based on Voting Classification architecture and a transformer, which increases the efficiency of polygraph testing and decreases the number of erroneous conclusions. The approach analyzes electrodermal resistance (galvanic skin response), blood vessel capacity (plethysmogram), and respiratory rhythms. This work represents a significant contribution to automated scoring systems and data analysis methods in polygraph testing.

Molchanov collaborates extensively with a core research team including Lev A. Derevyagin, Veniamin V. Makarov, Vladimir I. Tsurkov, and Andrey N. Yakovlev, with whom he has published multiple papers in the Journal of Computer and Systems Sciences International. He has received public awards for his work in the field. As principal of a polygraph examiner school at the International Academy of Lie Research, he plays a key educational role in training Russian polygraph examiners, with the school being one of the leading institutions in this area in Russia.

His work bridges traditional polygraph methodology with modern computational approaches, incorporating psychological theory into technical applications. His teaching emphasizes the application of established psychological theories—including A.N. Leontiev's activity theory and D.N. Uznadze's theory of set—to the practice of psychophysiological detection of deception, demonstrating an interdisciplinary approach that combines psychology, physiology, and computer science.