Dmitry I. Kaplun

Dmitry I. Kaplun

Ph.D: St. Petersburg Electrotechnical University “LETI”, 2009, St. Petersburg

Level of English language proficiency: Fluent in spoken and written English

Supervisor’s research interests:

  • Digital signal processing
  • Embedded systems
  • Machine Learning

The direction of training for which the graduate student will be accepted: Mathematics and Artificial Intelligence

List of research projects of a potential supervisor (participation/guidance)

Grants of the Russian Foundation for Basic Research:

  1. The youth initiative projects "My First Grant" “Developing and studying methods of digital filtering and intellectual analysis of information in the tasks monitoring of a wide frequency range” (agreement No. 12-07-31209, 13-0731209) for 2012-2013, PI.
  2. The scientific projects executed by young scientists under the leadership of Ph.D. and doctors of science in the scientific organizations of the Russian Federation in 2015 “Development and research of maximum likelihood digital filters for signals with partially overlapping spectra” (agreement No. 15-37-50902), PI.
  3. Russian-Israeli (RFBR-MOST) research projects “Video-based monitoring of health-related parameters for veterinary healthcare” (agreement No. 19-57-06007 for 2019-2022), PI.

Grants of the Russian Science Foundation:

  1. The scientific projects executed under the leadership of young scientists “Methods of adaptive processing and intellectual analysis of big data in hydroacoustic monitoring tasks” (agreement No. 17-71-20077 for 2017-2020), Co-PI (PI is Dmitry Klionskiy).
  2. The scientific projects executed by separate scientific groups “Advanced hardware with increased noise immunity for data processing and modeling of dynamic systems based on vector calculators” (agreement No. 19-19-00566 for 2019-2021), Co-PI (PI is Ivan Tyukin).
  3. The Base Project from the Ministry of Science and Higher Education of the Russian Federation under assignment No. 0788-2020-0002 for 2020-2024, Co-PI (PI is Mikhail Bogachev).
  4. PI in different projects with industrial partners like Siemens, ID R&D, Unilever.

List of possible research topics

  • Adaptive signal processing algorithms for non-stationary signals
  • Research for increasing performance of embedded AI systems with the use of Residue Number System
  • Ensemble-based deep learning models for expert-diagnostic systems

Research highlights

Experimental investigations will be carried out in the up-to-date technology laboratory, equipped by modern hardware-based GPU and FPGA and software-based Python, Matlab, LabView. We have the extended cooperation with several foreign teams, as well as with several leading Universities and laboratories in St. Petersburg, Moscow, Stavropol etc.

Supervisor’s specific requirements

Skills in data processing, hardware engineering and mathematics. Deep knowledge of undergraduate courses, such as:

  • Mathematical Foundations of Systems Theory
  • Microprocessors in Control Systems
  • Distributed Data Processing

Supervisor’s main publications

Total amount of papers indexed by WoS within the last 5 years is more than 50.

  1. Mukherkjee, D., Saha, P., Kaplun, D. et al. Brain tumor image generation using an aggregation of GAN models with style transfer. Sci Rep 12, 9141 (2022).
  2. Sarah-Elizabeth Byosiere, Marcelo Feighelstein, Kristiina Wilson, Jennifer Abrams, Guy Elad, Nareed Farhat, Dirk van der Linden, Dmitrii Kaplun, Aleksandr Sinitca, Anna Zamansky, Evaluation of shelter dog activity levels before and during COVID-19 using automated analysis, Applied Animal Behaviour Science, Volume 250, 2022, 105614, ISSN 0168-1591,
  3. Alexander Chikov, Nikolay Egorov, Dmitry Medvedev, Svetlana Chikova, Evgeniy Pavlov, Pavel Drobintsev, Alexander Krasichkov, Dmitry Kaplun, Determination of the athletes' anaerobic threshold using machine learning methods, Biomedical Signal Processing and Control, Volume 73, 2022, 103414,
  4. Bhattacharyya, A., Chatterjee, S., Sen, S., Kaplun, D., Sinitca, A., Sarkar, R. A deep learning model for classifying human facial expressions from infrared thermal images. Sci Rep 11, 20696 (2021). (
  5. Manna, A., Kundu, R., Kaplun, D., Sinitca, A., Sarkar, R. A fuzzy rank-based ensemble of CNN models for classification of cervical cytology. Sci Rep 11, 14538 (2021) (

Results of intellectual activity

  • Software complex “Hearing training”
  • Program for spectral analysis of non-stationary signals
  • Program for adaptive processing of multi-channel hydroacoustic signals
  • Method for determining area of intra-nasal anatomical holes
  • Vector signal filtering device