Andrei Lavrentyev

Andrei Lavrentyev

Head of Technology Development

Doctor (PhD) of Physical and Mathematical Sciences

Research areas: technologies to counter attacks on cyber-physical systems; searching for structures and anomalies in large time series; machine learning; deep neural networks; spiking neural networks; neuro-semantic networks.

  1. D. Shalyga, P. Filonov and A. Lavrentyev, “Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization”, arXiv.org, 2018. [Online]. Available: https://arxiv.org/abs/1807.07282. [Accessed: 28- Feb- 2020].
  2. P. Filonov, F. Kitashov and A. Lavrentyev, “RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process”, arXiv.org, 2017. [Online]. Available: https://arxiv.org/abs/1709.02232. [Accessed: 28- Feb- 2020].
  3. P. Filonov, A. Lavrentyev and A. Vorontsov, “Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model”, arXiv.org, 2016. [Online]. Available: http://arxiv.org/abs/1612.06676. [Accessed: 28- Feb- 2020].
  4. A. Lavrentyev, “MLAD: Machine Learning for Anomaly Detection”, Kaspersky ICS CERT, 2018. [Online]. Available: https://ics-cert.kaspersky.com/reports/2018/01/16/mlad-machine-learning-for-anomaly-detection. [Accessed: 28- Feb- 2020].
  5. A. Lavrentyev, “Neurosemantic approach and free energy minimization principle”, in The sixth international conference on cognitive science, Kaliningrad, 2014, pp. 68-70.
  6. A. Lavrentyev, “Dynamical processes inside neurosemantic hypernetwork and brain work principals”, in XI International interdisciplinary congress Neuroscience for medicine and psychology, Sudak, 2015, pp. 241-242.
  7. Kaspersky, CoLaboratory: Industrial Cybersecurity Meetup #3. 2017. [Online]. Available: https://youtu.be/CufKpjweoCs?t=4297. [Accessed: 28-Feb-2020].

Artem Vorontsov

Artem Vorontsov

Mathematician

Doctor (PhD) of Physical and Mathematical Sciences

Research areas: reinforcement learning, self-learning control systems, atmospheric optics, approximation theory.

  1. A.M. Vorontsov, G.A. Filimonov, “The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system”, ArXiv Preprint, 2022, https://arxiv.org/abs/2204.05227.
  2. A.M. Vorontsov, M.A. Vorontsov, G.A. Filimonov and E. Polnau, “Atmospheric turbulence study with deep machine learning of intensity scintillation patterns”, Appl. Sci. v.10, p. 8136. 2020. doi:10.3390/app10228136.
  3. P.V. Filonov, A.B. Lavrentyev and A.M. Vorontsov, “Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using the LSTM-based Predictive Data Model”, NIPS 2016, Barcelona.
  4. G.A. Filimonov, V.V. Kolosov, A.M. Vorontsov, “Computationally efficient methods for simulating laser heating of bulk plates”, Atmos. Ocean Opt. v.34. p. 617-624. 2021. https://doi.org/10.1134/S1024856021060087.
  5. P.V. Paramonov, A.M. Vorontsov, and V.E. Kunitsyn, “A three-dimensional refractive index model for simulation of optical wave propagation in atmospheric turbulence”, Waves in Random and Complex Media, v.25, No. 4, p. 556-575, 2015.
  6. A.M. Vorontsov, P.V. Paramonov, M. Valley and M.A. Vorontsov, “Generation of infinitely-long phase screens for modeling of optical wave propagation in atmospheric turbulence”, Waves in Random and Complex Media, v. Click to view volume18, Issue 1, p. 91 – 108, 2008.
  7. V.E. Kunitsyn, B.Yu. Krysanov and A.M. Vorontsov, “Acoustic-gravity waves in the Earth’s atmosphere generated by surface sources”, Moscow University Physics Bulletin, v.15. No 6, p. 541-546, 2015.
  8. V.E. Kunitsyn and A.M. Vorontsov, “Modeling the ionospheric propagation of acoustic gravity waves from the Tohoku tsunami of 2011”, Moscow University Physics Bulletin, v. 69, No. 3, p. 263-269, 2014.
  9. A.M. Vorontsov, “Joint Approximations of Distributions in Banach Spaces”, Mathematical Notes, v. 73, No. 2, p. 179-194, 2003.
  10. A.M. Vorontsov, “Estimates of -Capacity of Compact Sets in “, Mathematical Notes, v. 75, No. 6, p. 751-764, 2004.

Mikhail Kiselev

Mikhail Kiselev

Data Scientist

Doctor (PhD) of Technical Sciences

Research areas: spiking neural networks; neurocomputers.

  1. M. Kiselev, A. Lavrentyev, A Preprocessing Layer in Spiking Neural Networks – Structure, Parameters, Performance Criteria. Budapest: proceedings of IJCNN-2019, paper N-19450, 2019.
  2. M. V. Kiselev, A General Purpose Algorithm for Coding/Decoding Continuous Signal to Spike Form. Moscow: proceedings of International Conference on Neuroinformatics in Studies in Computational Intelligence, vol. 799, B. Kryzhanovsky, W. Dunin-Barkowski, V. Redko, Yu. Tiumentsev (Eds.), 2018, pp. 184-189.
  3. M. V. Kiselev, A Synaptic Plasticity Rule Providing a Unified Approach to Supervised and Unsupervised Learning. Anchorage: proceedings of IJCNN-2017, 2017, pp. 3806-3813.
  4. M. V. Kiselev, Rate Coding vs. Temporal Coding – Is Optimum Between? Vancouver: proceedings of IJCNN-2016, 2016, pp. 1355-1359.
  5. M. V. Kiselev, Conversion from Rate Code to Temporal Code – Crucial Role of Inhibition. St. Petersburg: proceedings of ISNN-2016 in LNCS 9719, L. Cheng, Q. Liu, A. Rozhnin (Eds.), St. Petersburg, 2016, pp. 665-672.
  6. M. V. Kiselev, Kiselev, Asynchronous-Polychronous Method for Coding Information in Spiking Neural Networks. Moscow: Neuroinformatics-2016 Conference proceedings, part 3, pp. 11–21.
  7. M. V. Kiselev, Kiselev, Application of Empirical Models for Constructing Spiking Neural Networks with Set Properties. Moscow: Neuroinformatics-2015 Conference proceedings, part 3, pp. 126–136.
  8. M. V. Kiselev, Kiselev, Computer Modeling of Spiking Neural Networks. Moscow: Lectures on Neuroinformatics at Neuroinformatics-2015 Conference, pp. 85–122.
  9. M. V. Kiselev, Empirical Models as a Basis for Synthesis of Large Spiking Neural Networks with Pre-specified Properties. Rome: proceedings of Conference on Neural Computation Theory and Applications (NCTA-14), 2014, pp. 264-269.
  10. M. V. Kiselev, Input-Output Characteristics of LIF Neuron with Dynamic Threshold and Short Term Synaptic Depression. Vienna: proceedings of ANNIIP 2014, K. Madani (Ed.), pp. 11-18.
  11. M. V. Kiselev, Homogenous Chaotic Network Serving as a Rate/Population Code to Temporal Code Converter. Computational Intelligence and Neuroscience, vol. 2014, Article ID 476580, 2014, 8 pages. doi:10.1155/2014/476580
  12. M. V. Kiselev, Kiselev, Homogeneous Chaotic Neural Network as a Converter from Asynchronous to Synchronous Signal Coding. Moscow: Neuroinformatics-2014 Conference proceedings, part 1, pp. 59–69.
  13. M. V. Kiselev, Self-Organization Process in Large Spiking Neural Networks Leading to Formation of Working Memory Mechanism. Proceedings of IWANN 2013 in LNCS 7902, I. Rojas, G. Joya, and J. Rojas, G. Joya, and J. Cabestany (Eds.), Part I, pp. 510-517.
  14. M. V. Kiselev, Kiselev, Formation of Polychronic Groups of Neuron Carriers of Short-Term Memory as a Result of the Evolution of Large Spiking Neural Networks. Moscow: Neuroinformatics-2013 Conference proceedings, part 1, pp. 82–91.
  15. M. V. Kiselev, Self-organized Short-Term Memory Mechanism in Spiking Neural Network. Ljubljana: proceedings of ICANNGA 2011 Part I, pp. 120-129.
  16. M. V. Kiselev, Kiselev, Formation of a Short-Term Memory Mechanism in Spiking Neural Networks Moscow: Neuroinformatics-2011 Conference proceedings, part 1, pp. 269–278.
  17. M. V. Kiselev, Self-organized Spiking Neural Network Recognizing Phase/Frequency Correlations. Atlanta, Georgia: proceedings of IJCNN’2009, pp. 1633-1639.
  18. M. V. Kiselev, Kiselev: Single-Layer Self-Organizing Spiking Neural Network Recognizing Fuzzy Synchrony in the Input Signal. Neurocomputer, No. 10, 2009, pp. 3–11.
  19. M. V. Kiselev, M. M. Shmulevich, A. I. Erlikh, An Automatic Text Clustering Method and Its Application. Software Products and Systems, No. 2, 2008, pp. 47–48.
  20. M. V. Kiselev, Kiselev, Text Clustering Method Based on the Pairwise Proximity of Terms Characterizing Texts and Its Comparison with Metric-Clustering Methods. Moscow: Yandex Publishing House, Internet Mathematics, 2007, pp. 74–83.
  21. M. V. Kiselev, Kiselev, SSNUMDL – Network of Stabilizing Spiking Neurons Recognizing Spatial-Temporal Images. Neurocomputer, No. 12, 2005, pp. 16–24.
  22. M. V. Kiselev, Kiselev, Optimizing the Procedure for Automatic Web Catalog Filling. Moscow: Yandex Publishing House, Internet Mathematics, 2005, pp. 342–363.
  23. M. V. Kiselev, V. S. Pivovarov, M. M. Shmulevich: Text Clustering Method Taking into Account the Cooccurrence of Key Terms and Its Application in Analyzing the Thematic Structure of News Flow and Dynamics. Moscow: Yandex Publishing House, Internet Mathematics, 2005, pp. 412–435.
  24. M. V. Kiselev, Statistical Approach to Unsupervised Recognition of Spatio-temporal Patterns by Spiking Neurons. Portland, Oregon: proceedings of IJCNN-2003, pp. 2843-2847.
  25. M. V. Kiselev, S. M. M. Shmulevich: Symbolic Knowledge Acquisition Technology: The Next Step in Data Mining. PC AI, v 13(1), 1999, pp. 48-51.
  26. M. V. Kiselev, S. M. Ananyan, S. B. Arseniev, LA – a Clustering Algorithm with an Automated Selection of Attributes, which is Invariant to Functional Transformations of Coordinates. Proceedings of 3rd European Symposium on Principles of Data Mining and Knowledge Discovery PKDD’99 in: Lecture Notes in Artificial Intelligence 1704, Springer, 1999, pp. 366-371.
  27. M. V. Kiselev, S. M. Ananyan, S. B. Arseniev, PolyAnalyst Data Analysis Technique and Its Specialization for Processing Data Organized as a Set of Attribute Values. Proceedings of 2nd European Symposium on Principles of Data Mining and Knowledge Discovery PKDD’98 in: Lecture Notes in Artificial Intelligence 1510, Springer, 1998, pp. 352-360.
  28. M. V. Kiselev, E. Solomatin, Applications of Data Mining Technology in Business and Finance. Open Systems, vol. 24(4), 1997, pp. 41–44.
  29. M. V. Kiselev, S. M. Ananyan, S. B. Arseniev, Regression-Based Classification Methods and Their Comparison with Decision Tree Algorithms. Trondheim, Norway: proceedings of 1st European Symposium on Principles of Data Mining and Knowledge Discovery, Springer, 1997, pp. 134-144.
  30. M. V. Kiselev, S. M. Ananyan, S. B. Arseniev, New Efficient Technology for Securities Portfolio Management: Data Mining Approach. Charlotte, NC: proceedings of ISMIS’97 (Tenth International Symposium on Methodologies for Intelligent Systems), 1997.
  31. M. V. Kiselev, State Treasury Bill/Federal Loan Bond Portfolio Management Technology Based on the Use of the PolyAnalyst Data Analysis System. Banking Technologies, vol. 22(10), 1996, pp. 86–88.
  32. M. V. Kiselev, S. B. Arseniev, Effective Portfolio Management Technology using the PolyAnalyst Data Analysis System. Kazan: Artificial Intelligence-96 Conference proceedings,1996, part 3, pp. 503–507.
  33. M. V. Kiselev, S. B. Arseniev, Discovery of Numerical Dependencies in the Form of Rational Expressions. Zakopane, Poland: proceedings of ISMIS’96, Springer, 1996, pp. 134-145.
  34. S. B. Arseniev, M. V. V. Kiselev, L. Vanina, A. Knyazev, Application of Machine Discovery System PolyAnalyst to Modeling Electron Density Distribution in Ionospheric Region D. Boulder, Colorado, USA: proceedings of IUGG-95 Conference; IASPEI/SEG Symposium (S13): Application of Artificial Intelligence Computing in Geophysics. 1995, (with).
  35. S. B. Arseniev, B. Classen, M. V. Kiselev, Patient Ventilation Management Expert Rules Derived from Ulm University Clinic Using PolyAnalyst – Knowledge Discovery System. Heraklion, Greece: proceedings of ECML-95 Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, 1995, pp. 199-203.
  36. M. V. Kiselev, PolyAnalyst 2.0: Combination of Statistical Data Preprocessing and Symbolic KDD Technique. Heraklion, Greece: proceedings of ECML-95 Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, 1995, pp. 187-192.
  37. S. B. Arseniev, E. V. Flerov, M. V. Kiselev, Automated Acquisition of Smart Alarm Rules from Monitoring Data Using the PolyAnalyst Machine Discovery System. Porto Carras, Greece: abstracts of 5th Symposium of European Society for Computing and Technology in Anaesthesia and Intensive Care (ESCTAIC-94), 1994, p. H6.
  38. S. B. Arseniev, M. V. Kiselev, PolyAnalyst – a Machine Discovery System for Intelligent Analysis of Clinical Data, Porto Carras, Greece: abstracts of ESCTAIC-94, 1994, p. H5.
  39. M. V. Kiselev, PolyAnalyst – a Machine Discovery System Inferring Functional Programs. Seattle: proceedings of AAAI Workshop on Knowledge Discovery in Databases’94, 1994, pp. 237-249.
  40. G. S. Asanov, M. V. Kiselev, Finslerian Analog of the Higgs Mechanism, Russian Physics Journal, v 32(6), 1989, pp. 451-454.
  41. G. S. Asanov, M. V. Kiselev, Comparing the Finslerian Gauge Approach with the SU(3)×SU(2)×U(1)-Model. Reports on Mathematical Physics, v 26(3), 1988, pp. 401-411.

Ekaterina Kazimirova

Ekaterina Kazimirova

Research Relations Manager, Researcher

Alumna of Faculty of Biophysics at Moscow State University

Research areas: cognitive architectures; biosimilar models of intelligent systems; creative intelligent systems.

  1. E. Kazimirova, “Two-Component Scheme of Cognitive System Organization: the Hippocampus-Inspired Model”, in IARIA, Cognitive-2017: Proceedings of The Ninth International Conference on Advanced Cognitive Technologies and Applications, 2017, pp. 21-23. [Online]. Available: https://www.researchgate.net/publication/314245303_Two-Component_Scheme_of_Cognitive_System_Organization_the_Hippocampus-_Inspired_Model. [Accessed: 28- Feb- 2020]
  2. E. Kazimirova, “Image Transformations in a Cognitive System. Tunnel transition and combining ensembles.”, in IARIA, Cognitive-2018: The Tenth International Conference on Advanced Cognitive Technologies and Applications, 2018, pp. 30-33. [Online]. Available: https://www.researchgate.net/publication/323756612_Image_Transformations_in_a_Cognitive_System_Tunnel_transition_and_combining_ensembles. [Accessed: 28- Feb- 2020]
  3. E. Kazimirova, “Human-Centric Internet of Things. Problems and Challenges.”, in INTELLI 2017. The Sixth International Conference on Intelligent Systems and Applications., Nice, France, 2017, pp. 72-74. [Online]. Available: https://www.researchgate.net/publication/319059870_Human-Centric_Internet_of_Things_Problems_and_Challenges. [Accessed: 28- Feb- 2020]