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
Publications
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Book
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Article
tDCS and neurofeedback in ADHD treatment
Attention deficit hyperactivity disorder (ADHD) stands as one of the most prevalent neurodevelopmental disorders, affecting millions worldwide. While traditional pharmacological interventions have been the cornerstone of ADHD treatment, emerging novel methods such as transcranial Direct Current Stimulation (tDCS) and neurofeedback offer promising avenues for addressing the multifaceted challenges of ADHD management. This review paper critically synthesizes the current literature on tDCS and neurofeedback techniques in ADHD treatment, elucidating their mechanisms of action, efficacy, and potential as adjunct or alternative therapeutic modalities. By exploring these innovative approaches, this review aims to deepen our understanding of neurobiological underpinnings of ADHD and pave the way for more personalized and effective interventions, ultimately enhancing the quality of life for individuals grappling with ADHD symptoms.
Frontiers in Systems Neuroscience. 2025. Vol. 19.
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Book chapter
ICIP: SESSIONS
This work introduces SCL-GAN (Spatially-Correlative Lightweight GAN), a novel architecture for facial image reconstruction using thermal face images, designed for efficient execution on edge devices such as the NVIDIA Jetson board. The proposed architecture leverages spatial feature correlations across thermal-visible modalities while maintaining a low parameter count and FLOPs. Experimental results show that SCL-GAN achieves a 68.57% reduction in computational cost (GMac) and a 71.71% reduction in trainable parameters, compared to baseline models. Moreover, we observe consistent improvements in image quality metrics, including a 5.05% increase in SSIM, 4.49% reduction in VGG-FaceLoss, and a 27.83% reduction in FID on the WHU-IIP dataset. On the CVBL-CHILD dataset, SCL-GAN demonstrates an 11.70% SSIM improvement, 18.21% VGG-FaceLoss reduction, and a 47.88% drop in FID. The code is available at: https://github.com/GANGREEK/SCL-GAN.git.
In bk.: 2025 IEEE International Conference on Image Processing (ICIP). Vol. XXIII. Iss. IX. IEEE, 2025. P. 2049-2054.
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Working paper
Versions of least-squares k-means algorithm for interval data
Recently, k-means clustering has been extended to the so-called interval data. In contrast to conventional data case, the interval data feature values are intervals rather than single reals. This paper further explores the least-squares criterion for k-means clustering to tackle the issue of initialization, that is, finding a proper set of initial cluster centers at interval data clustering. Specifically, we extend, for the interval data, a Pythagorean decomposition of the data scatter in the sum of two items, one being a genuine k-means least-squares criterion, the other, a complementary criterion, requiring the clusters to be numerous and anomalous. Therefore we propose a method for one-byone obtaining anomalous clusters. After a run of the method, we start k-means iterations from the centers of the most numerous of the found anomalous clusters. We test and validate our proposed BIKM algorithm at versions of two newly introduced interval datasets.Математические методы анализа решений в экономике, бизнесе и политике. WP7. Издательский дом ВШЭ, 2024