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
Spatiotemporal Dynamics in the Network of Memristive Morris–Lecar Neurons with Chemical Synapses
The disruption of the human brain’s resting states, which are critical for normal cognitive function and information processing, has been frequently linked to alterations in spatiotemporal coordination. Among the most prominent patterns in such excitable neural media are spiral waves, whose formation and dynamics have been extensively studied across biological and com putational contexts. In this regard, many studies have focused on electrical synapses in the spatially connected neuronal networks. In this study, we investigate the dynamics of a lattice of memristive Morris–Lecar neurons under the influence of chemical synaptic coupling. Specifi cally, we report the distinct wave patterns that emerge when the key chemical synaptic parame ters, such as firing threshold, sigmoidal slope and reversal potential, are varied. Additionally, we investigate the influence of electromagnetic induction, external current, noise effects and other factors that drive the emergence of spiral waves. We have computed the statistical synchroniza tion factor for the key parameter of the chemical synapse. We observe diverse spatiotemporal patterns; notably, the single-core spirals are prominent in the excitable media. Furthermore, these spirals frequently transition into turbulent patterns or vanish altogether. These results advance our understanding of the role of chemical synapses in neuron populations. Our results may offer novel perspectives on spiral wave mechanisms and related neurobiological research.
International Journal of Bifurcation and Chaos in Applied Sciences and Engineering. 2026. P. 1-19.
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Book chapter
Lacuna Inc. at SemEval-2026 Task 4: Structurally Gated State-Space Models for Disentangling Narrative Similarity
In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM),our submission to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning. Evaluating narrative similarity is a profound computational challenge that requires models to look past concrete, superficial elements such as specific names, actors, objects, or settings to isolate and compareabstract patterns of causality and plot progression. To model these extended causal chainswithout the quadratic bottlenecks of standard Transformers, we leverage a hybrid State-SpaceModel (Jamba-1.5-Mini). Building upon this backbone, we introduce the Structurally Gated Alignment (SGA) head, a novel, differentiable algorithmic architecture. The SGA head operates on two scales: a heavily strided Macro-path maps the coarse structural skeleton of a story, which then acts as a gating mechanism to filter a full-resolution Micro-path, actively suppressing semantic noise and superficial keyword overlaps. Evaluated on both pairwisecomparative judgments (Track A) and dense representation learning (Track B), our approach demonstrates that explicitly disentangling structural invariants from lexical variants provides a robust, principled framework for deep narrative understanding.
In bk.: Proceedings of the 20th International Workshop on Semantic Evaluation (2026). San Diego: Association for Computational Linguistics, 2026. P. 2347-2353.
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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