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Contract number
14.Y26.31.0022, 075-15-2021-634
Time span of the project

As of 01.11.2022

Number of staff members
scientific publications
Objects of intellectual property
General information

Name of the project: Scalable networks of AI systems for analysis of data of increasing dimensionality

Goals and objectives

Research directions: Computer and information sciences

Project objective: Developing prospective methods of intellectual high dimensional data analysis optimized for working with high dimensional (tens and hundreds) to very high dimensional (thousands, tens of thousands) dimensions

The practical value of the study

Scientific results:

The result of the implementation of the project is the creation of a new world-class laboratory on the grounds of the Nizhniy Novgorod State University devoted to methods of the analysis of multi-dimensional data. This resulted in the solution of scientific problems and the development of research domains joint by the common united by the common goal of developing promising methods of the intelligent analysis of high-dimensionality data optimized for working in high (tens and hundreds) and very high (thousands, tens of thousands and more) dimensionality.

The particular results of our research are:

  1. A software and hardware complex for electrocardiographic measurements (a patent for invention).
  2. A technology for the automated refueling of drones (a patent for invention).
  3. A technology for registering human emotional disadaptation with the use of cardiorhythmography (a patent for invention).
  4. A technology for registering post-COVID states with the use of cardiorhythmography (a patent for invention).
  5. A new system of dynamic models for forecasting processes of growth, separation, processing and the visualization of experimental data on the dynamics of individual cells. 
Education and career development:
  • Within the International Congress on Computational Intelligence at the International Joint Conference on Neural Networks (IJCNN) we have organized the special section  «Neural intelligence of tomorrow» to present the results of the project (7-15 July 2018, Rio de Janeiro, Brazil).

At the International Joint Conference on Neural Networks (IJCNN) we organized the special section «Metrology of AI: blessing of dimensionality, tolerance and fits» to present the results of the project (13-18 July 2019, Budapest, Hungary).

At the international conference «Neuroinformatics 2019» we organized the international seminar «Geometry of Big Data». Keynotes by employees of the Laboratory were presented at the conference. Information on the conference can be found at http://neuroinfo.ru/index.php/ru/info/progcomitee. The leading scientist Alexander N. Gorban was a co-chair of the program committee of the conference (7-11 October 2019, Dolgoprudniy, Russia).

We organized the International Conference «Neural Networks of Tomorrow: Problems and Prospects» (30 November — 2 December 2019, Nizhniy Novgorod, «City Hotel Sova»). The work of the section was staged in several formats, including keynotes by the participants, lectures, seminars and round tables. The event featured keynotes by employees of more than 40 specialized institutions from 11 Russian cities as well as from Belarus, Kazakhstan, Germany, the United Kingdom and Iran. 44 keynotes and one special lecture were delivered. The program of the conference, abstracts of presentations and keynotes can be found at the website of the conference http://conf.neuro.unn.ru/nn2019/

Within the conference «IEEE World Congress on Computational Intelligence» (WCCI) we staged the special section «Validation, Explanation, And Correction Of Artificial Intelligence Systems» under the supervision of Alexander N. Gorban and Ivan Yu. Tyukin, where, in particular, 6 presentations were presented covering the results of the project (19-24 July 2020, Glasgow, United Kingdom).

Within the International Joint Conference on Neural Networks (IJCNN) we organized the special section «Metrology, Verification, and Explanation of Data-Driven AI Systems and Neural Networks» to present the results of the project (18-22 July 2021, Shenzhen, China PR).

Within the International Conference «Volga Neuroscience meeting 2021» we organised the special sections «Computational Neuroscience» and «Neurodynamics And Artificial Intelligence» (24-27 August 2021, Nizhniy Novgorod, Russia). Information on the conference can be found at: http://conf.neuro.unn.ru/vnm-2021/.

As part of the forum «Neuroscience, Artificial intelligence and complex systems» we organized  the special section «Artificial Intelligence» (13-15 September 2021, Kaliningrad, Russia). Information concerning the conference  can be found at http://bfnaics.kantiana.ru/.

We also organized events devoted to our area of research: the 25th All-Russian Seminar «Modeling of Non-equilibrium, Adaptive and Managed Systems - 2022» (7–9 October 2022, Krasnoyarsk, Akademgorodok, Russia), the 30th All-Russian seminar  «Neuroinformatics, its Applications and Data Analysis» (30 September–2 October 2022, Krasnoyarsk, Akademgorodok, Russia), the 24th International Science and Technology Conference «Neuroinformatics-2022» (17-21 October 2022, Moscow Institute of Physics ant Technology, Dolgoprudniy, Russia).

  • In 2020-2021, one Candidate of Science dissertation and 2 master's degree theses were prepared and defended.
  • In 2018-2019, 7 employees of the Laboratory completed internships in the area of research of our Laboratory at leading Russian and international  research and education centers: the Complutense University of Madrid (Spain), the University of Leicester, Alexander N. Gorban's Center of Mathematical Modeling in Leicester (United Kingdom), the German Center for Neurodegenerative Diseases (Germany), the Saint Petersburg State University, the Moscow State University, the Skolkovo Institute of Science and Technology (Russia).
  • We have prepared special lecture courses for the bachelor's degree program of the Institute of Biology and Biomedicine of the Nizhniy Novgorod State University: «Architecture of sensory signal processing pathways in the brain» and «Foundations of artificial intelligence and data analysis».

In view of the achieved results of the project we have modified the special course «Modeling of brain neural networks» delivered by the employee of the Laboratory Sergey A. Lobov for bachelor’s degree students majoring in Biology.

We are preparing to transfer two one-semester video courses to the Nizhniy Novgorod State University: «Mathematical Modelling» and «Data Mining and Neural Networks» delivered by the leading scientists Alexander N. Gorban in the United Kingdom for master’s degree students  majoring in «Data Analysis for Business Intelligence». 

On 16 April 2019 Alexander N. Gorban  delivered a popular lecture devoted to the project for a wide audience of students, postgraduates and research fellows at the Institute of Applied Physics of the Russian Academy of Sciences. The lecture  was dedicated to the developments pursued  in the field of artificial intelligence, the existing limitations and challenges that can impede the creation of artificial intelligence .

Alexander N. Gorban delivered a master class in collaboration with Professor Artyom R. Oganov for young researchers — participants of the 3rd International Conference «Science of the Future» (13-18 May 2019, Sochi, Russia).

The leading scientist Alexander N. Gorban actively remotely participates in other public events and expert evaluations in Russia. In particular:

  1. He delivered the lecture «Frontier engineering problems with examples from artificial intelligence development» at the conference «Modern training of engineers» (22–23 June 2020, on the grounds of the Center for the National Technical Initiative of the Saint Petersburg State University).
  2. He was an invited expert at the discussion with the participants of the group «Artificial Intelligence» within the project of strategic development of the Saint Petersburg Electrotechnical University «LETI» in this domain (28 May 2020, on the grounds of the Center for the Transformation of Education of the Moscow School of Management Skolkovo).
  3. He delivered the lecture «The human and artificial intelligence — the birth of a centaur» within the month of the National Technical Initiative and the extracurricular activity movement of the National Technical Initiative and the preparation for the forum «Strong ideas for the new times». The lecture has been viewed by more than 300 thousand people at the internet portal «Big Change» (22 August 2020) https://forum.asi.ru/с
  4. He read the lecture «Quo Vadis, AI? Who are we, where are we going, how do we gauge our path» at the «Day of Science in Russia» (8 February 2021).
  5. He delivered the lecture «Dynamic phenotypes of diseases: trajectories of diseases, their branching and crises (data analysis)» at the conference «Healthy aging and age-related diseases» (25 February 2021).
  6. He delivered the lecture «Data-driven artificial intelligence: working on errors» within the project «At the bleeding edge of science» (16 May 2021) https://vk.com/video-174958021_456239059
  7. As part of the preparation for the World Amateur Go Championship Professor Gorban consulted the hackathon «Games of Minds» and the Centaurs Go Match («human-artificial intelligence»). The match of centaurs was successfully staged at the 41st World Amateur Go Championship (7-8 June 2021) https://centergo.ru/igra-go-i-ii/
  8. He delivered the lecture «What happening on the labor market? How does education participate in it? What will happen tomorrow?» within the discussion «New professions in the era of digitalization» WoldSkills Russia - 2021 http://www.center-rpo.ru/2-uncategorised/1562-diskussiya-novye-professii-v-epokhu-tsifrovizatsii
  • The «Physics of Life Reviews» journal (2017  impart factor = 13.783) published  a discussion by leading international experts about our work on the project.   


  • Department of Cancer Bioinformatics at Institut Curie (France), Department of Mathematics of the University of Leicester (United Kingdom), Molecular Pathology Unit at the Pathology Research Center of Massachusetts General Hospital and Harvard Medical School, Department of Biostatistics and Computational Biology of the Dana-Farber Cancer Institute, Department of Biological Engineering of the Massachusetts Institute of Technology, Harvard Stem Cell Institute (USA), Department of Computer Science and Technology of Tongji University, Shanghai Key Lab of Intelligent Information Processing and the School of Computer Science of Fudan University (China PR), Department of Mathematics and Computer Science of the University of Palermo, Department of Sciences for Technological Innovations of the Euro-Mediterranean Institute of Science and Technology (Italy), Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences (Russia): joint research for the creation and enhancement of a technology for processing biological data of a new type – transcriptomes of a large number of separate cells.
  • German Center for Neurodegenerative Diseases (Germany), Complutense University of Madrid (Spain), Saint Petersburg State University, Skolkovo Institute of Science and Technology, Institute of Applied Physics of the Russian Academy of Sciences (Russia): joint research in the domain of mathematical modeling and development of artificial neural networks. 

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a.n. gorban, a. golubkov, b. grechuk, e.m. mirkes, i.y. tyukin
Correction of AI systems by linear discriminants: Probabilistic foundations, Information Sciences 466 (2018), 303-322 – (IF 2016 4.832, Q1 in Computer Science, Information Systems).
h chen, l albergante, jy hsu, ca lareau, gl bosco, j guan, s zhou, an gorban, de bauer, mj aryee, dm langenau, a zinovyev, jd buenrostro, g-c yuan, l pinello
Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nature Сommunications. 2019 Apr 23;10(1):1903 https://doi.org/10.1038/s41467-019-09670-4. (IF 2017 11.878, Q1 in Multidisciplinary Sciences).
an gorban, va makarov, iy tyukin
The unreasonable effectiveness of small neural ensembles in high-dimensional brain, Physics of Life Reviews, 2019, https://doi.org/10.1016/j.plrev.2018.09.005. (IF 2017 11.045, Q1 in biology and biophysics, the most cited journal in these categories)
an gorban, r burton, i romanenko, iy tyukin
One-trial correction of legacy AI systems and stochastic separation theorems, Information Sciences 484 (2019) 237–254 https://doi.org/10.1016/j.ins.2019.02.001. (IF 2018 5.524, Q1 in computer science, information systems).
iy tyukin, an gorban, s green, d prokhorov
Fast Construction of Correcting Ensembles for Legacy Artificial Intelligence Systems: Algorithms and a Case Study https://doi.org/10.1016/j.ins.2018.11.057. Information Sciences 485 (2019), 230-247 – (IF 2018 5.524, Q1 in computer science, information systems).
ev pankratova, ai kalyakulina, sv stasenko, sy gordleeva, ia lazarevich, vb kazantsev
Neuronal synchronization enhanced by neuron–astrocyte interaction. Nonlinear Dynamics, 97, 647–662 (2019) https://doi.org/10.1007/s11071-019-05004-7. (IF 2018 4.604, Q1 In Engineering).
a. n. gorban, e. m. mirkes, i. y. tyukin.
How Deep Should be the Depth of Convolutional Neural Networks: a Backyard Dog Case Study. Cognitive Computation. https://doi.org/10.1007/s12559-019-09667-7 (IF=4.287, Q1 in Computer science, artificial intelligence)
gorban, a.n., tyukina, t.a., pokidysheva, l.i. and smirnova, e.v.
Dynamic and thermodynamic models of adaptation. Physics of Life Reviews, Volume 37, 2021, Pages 17-64. https://doi.org/10.1016/j.plrev.2021.03.001 (IF 2020 11.025, Q1 in Biophysics)
kastalskiy, i.a., pankratova, e.v., mirkes, e.m. et al.
Social stress drives the multi-wave dynamics of COVID-19 outbreaks. Sci Rep 11, 22497 (2021). https://doi.org/10.1038/s41598-021-01317-z (IF 2020 4.380, Q1 in Multidisciplinary Sciences)
wang, s., celebi, m. e., zhang, y. d., yu, x., lu, s., yao, x., ... & tyukin, i.
Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects. Information Fusion. https://doi.org/10.1016/j.inffus.2021.07.001 (IF 2020 12.975, Q1 in Computer Science, Artificial Intelligence)
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