The centre's main objectives are:
1
developing and advancing interpretable machine learning and data mining methods for NLP and recommender systems
2
developing models that enhance the functionality of existing large language models by leveraging additional resources: linguistic models, knowledge models, search models, and planning algorithms
3
developing models and methods for automatic knowledge acquisition using large language models (LLM), including methods for transfer learning between different languages and different tasks
4
developing models and methods for research, modelling, and analysis within the framework of complex systems theory
5
developing semantic analysis tools based on mathematical methods in formal concept theory
Structure
International Laboratory of Intelligent Systems and Structural Analysis
We conduct research that enables the integration of structural and neural network representations in applied data analysis tasks
Laboratory of Models and Methods of Computational Pragmatics
We work on natural language processing (NLP), interpretable machine learning, and data mining, develop recommender systems and services, and advance multimodal clustering and classification methods that enable the creation of user interest profiles across multiple modalities
Laboratory of Complex Systems Modelling and Control
We conduct fundamental and applied scientific research in the mathematical modelling of complex systems, studying synchronisation phenomena, sudden regime changes, quasi-regularities, self-organisation, evaluating the effectiveness of rare event forecasting algorithms, and managing complex systems
Semantics Analysis Laboratory (in Russian)
Study of natural language as a whole within the natural science paradigm using methods of computer science and applied mathematics
Management
Director of the Centre, Doctor of Sciences, Professor
Deputy Director of the Centre, Candidate of Sciences
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Publications
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Book
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Article
Sandpile Cellular Automata with Manna Rule on the Chung-Lu Graph: Application to X Social Network
Numerous studies have provided compelling evidence that the probability distribution of avalanche sizes follows a power law, and that 1/f noise is present in the stochastic dynamics of landslides. We present a theoretical explanation of these phenomena within the framework of self-organized criticality theory, proposing a discrete-time model in the form of a sandpile cellular automaton on a fractal landscape. We also introduce temporal and spatial early warning signals associated with critical slowing down of the landscape as it self-organizes into a critical state exhibiting avalanche-like dynamics. The results of this study may be useful for those involved in landslide risk assessment and the development of landslide early warning systems.
Lecture Notes in Civil Engineering. 2026. Vol. 886. P. 249-255.
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Book chapter
Sandpile Cellular Automata with Manna Rule on the Chung-Lu Graph: Application to X Social Network
A discrete-time model is presented for the self-organization of the X/Twitter social network to the edge of a phase transition, characterized by avalanche-like propagation of information in the network. The model is based on the analogy between the critical dynamics of the X social network and sandpile cellular automata with Manna rule on the Chung-Lu graph. An empirical justification is presented for the self-organization of the segment of the X social network to the edge, which unites network users who post and repost content relevant to the 2016 and 2020 United States presidential debates. The time intervals during which the network is on the edge have been established. A comparison of quantitative indicators of the criticality of real and model time intervals was used to validate the discrete-time model.
In bk.: Studies in Computational Intelligence. Vol. 1265: Complex Networks & Their Applications XIV. Springer, 2026. Ch. 1265. P. 266-273.
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Working paper
Hessian-based lightweight neural network for brain vessel segmentation on a minimal training dataset
Accurate segmentation of blood vessels in brain magnetic resonance angiography (MRA) is essential for successful surgical procedures, such as aneurysm repair or bypass surgery. Currently, annotation is primarily performed through manual segmentation or classical methods, such as the Frangi filter, which often lack sufficient accuracy. Neural networks have emerged as powerful tools for medical image segmentation, but their development depends on well-annotated training datasets. However, there is a notable lack of publicly available MRA datasets with detailed brain vessel annotations. To address this gap, we propose a novel semi-supervised learning lightweight neural network with Hessian matrices on board for 3D segmentation of complex structures such as tubular structures, which we named HessNet. The solution is a Hessian-based neural network with only 6000 parameters. HessNet can run on the CPU and significantly reduces the resource requirements for training neural networks. The accuracy of vessel segmentation on a minimal training dataset reaches state-of-the-art results. It helps us create a large, semi-manually annotated brain vessel dataset of brain MRA images based on the IXI dataset (annotated 200 images). Annotation was performed by three experts under the supervision of three neurovascular surgeons after applying HessNet. It provides high accuracy of vessel segmentation and allows experts to focus only on the most complex important cases. The dataset is available at https://git.scinalytics.com/terilat/VesselDatasetPartly.Statistical mechanics. arXie. arXive, 2025