Research Group

We help to research advanced models of Artificial

Intelligence in the following directions:

Generative models
We study the problems of generative models: time frame-by-frame connectivity of the generated video, convergence of models during training in denoising tasks, interpretability and controllability of generation processes within codecs.
Predictive analytics
We create diagnostic methods for complex systems for timely and preventive impact.
Computer Vision
We investigate complex architectures (img2img, Transformer) for detection, segmentation, resolution enhancement, 3D reconstructions, etc.
Zero- and Few-shot approaches
We solve problems with a significant lack of data: there are several examples in the sample with a large number of classes.
We apply small-choice approaches to computer vision tasks (video stream detection and segmentation) and natural language processing (text classification and tagging).
NLP Analytics
We create a complete set of text analysis methods: hard and soft clustering, summarization, classification and tagging, search tasks.
Wearable devices
We apply algorithms to data from inertial and biomedical sensors to solve problems of HAR, pressure detection, navigation, etc.
Working with sound
We use generative and discriminative deep learning approaches for denoising audio tracks, changing their style and detecting emotions.
We study the applicability of AI methods to encoding information of various nature: from the video stream to the human genome.
Interpreted models
We create deep learning models that can be interpreted and managed in the process of use.
Clients of research teams form the goals of R&D projects. We have highlighted the most common research requests:
  • Preliminary Research
    Preliminary and detailed study of the subject area with the search for scientific articles, available data, and open source in order to obtain a base of materials for a quick dive into a new area and form requirements for future R&D results
  • Technological Pipeline Development
    Development of a complete technological pipeline for solving a problem based on client data, based on existing algorithms and open source in order to obtain a strong basic solution for subsequent modifications and growth of target metrics
  • Negative Effect Removal
    Elimination of a key negative effect in the operation of the existing model: from excessive sensitivity to fluctuations in the data to the quality of work in corner cases. The goal is to grow certain metrics of the customer model within the given constraints
  • Industrial Pipeline Development
    Development of a Proof-of-the-Concept pipeline for solving an industrial business problem based on artificial intelligence algorithms in the absence of known approaches to its solution. The goal is the fundamental possibility of solving such a problem
The methodology for the implementation of R&D projects developed on the basis of our experience allows us to quickly carry out a full cycle of research and reduce risks
Flexible approach to managing the project scope and transparency of the development process
Using the methodology of AI projects to reduce risks and systematically achieve breakthrough results
Deep understanding of both basic business needs and the use of SotA AI methods
Short iterations with the transfer of a useful and exploitable result
Research results are fully transferred to the client
  • Software implementation
    Easy to use library for with readable and reproducible code
  • Materials base
    Base of materials with reviews for a quick dive into the field and a technical report
  • Trained Models
    Parameters of trained models packed in the format required by the client
  • Anything else
    We can prepare project artifacts in the format required by the customer
MIL Team Research conducted research for
Scientific consultants of the Research Group
  • Konstantin Vorontsov
    Prof, MSU
    Specialisation: NLP
  • Vadim Strijov
    Prof, Grenoble
    Specialisation: Sensors
  • Mikhail Burtsev
    Prof, AIRI
    Specialisation: NLP
  • Radoslav Neychev
    Specialisation: Sensors
  • Andrey Leonidov
    Prof, CERN
    Specialisation: ML
  • Andrey Raygorodsky
    Prof, Yandex
    Specialisation: ML, Graphs
  • Ilya Zharikov
    Specialisation: DL & Sensors
  • Oleg Bakhteev
    Specialisation: DL
Some Research Results
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