CDISE research activities are distributed among the following research groups:

  • Advanced Data Analytics in Science and Engineering (ADASE) (Prof. Evgeny Burnaev)
    ADASE group research is about developing efficient machine learning methods and their use for solution of applied engineering problems and industrial analytics. In particular, we are working on regression based on Gaussian processes and kernel methods for multi-fidelity surrogate modeling and optimization, Deep Learning for 3D Data Analysis and manifold learning, on-line sequence learning for prediction and non-parametric anomaly detection.
  • AeroNet Lab (Dr. Vladimir Ignatiev)
    The research of the lab is devoted to the creation of an open benchmark for remote sensing data and their products – a set of images with the labels of different classes, obtained from various aircraft, in particular, satellite imagery, UAV and MA survey. Our lab activities are  to build adaptation procedures of modern intelligent algorithms for data analysis, and also to develop fundamentally new approaches to the processing of multidimensional/multi-fidelity remote sensing data (semantic segmentation, classification, detection of changes, consolidation of heterogeneous data, etc.)
  • Applied Information Theory Group (Prof. Alexey Frolov)
    The group researches the border of information theory, communications and machine learning with a particular focus on the application of the research results in communications, including engineering and industry problems.
  • Computational Molecular Science Group (Prof. Maxim Fedorov)
    The primary research interest of the group is in finding new ways in chemical informatics that are based on a combination of physical chemistry methods with the machine learning techniques for prediction of properties of organic compounds. Our primary goal is to develop methods that, on the one hand, are accurate and, on the other hand, are universal and have wide applicability domains.
  • Computer Vision (Prof. Victor Lempitsky)
    Computer vision group research is about designing computer systems that can extract, organize, and quantify information contained in images of various types and origin. For this purpose, the group develops new machine learning techniques (deep learning in particular) and optimization techniques that are robust and flexible enough to handle and to adapt to the diversity of image data in the modern world.
  • Laboratory “Tensor networks and deep learning for application in data mining” (Prof. Andrzej Cichocki)
    The Laboratory develops new fundamental approaches for training, testing and storing parameters of deep neural networks based on tensor decomposition techniques. These approaches allow to reduce by orders of magnitude computational complexity and required memory for the operation of the network while maintaining a high quality of prediction. The Laboratory mission is to pursue cutting-edge research in the design and analysis of deep neural networks, tensor decompositions, tensor networks and multiway analysis with many potential practical applications.
    The Laboratory brings together several professors and young researchers in the fields of machine learning, computer vision, artificial intelligence, robotics, large-scale data analysis, mathematics as well as computational neuroscience and bioinformatics.
  • Mass Spectrometry Lab (Prof. Evgeny Nikolaev)
    The Laboratory is focused on the instrumentation development and application of state of the art mass spectrometry techniques. All projects are closely related to modeling of ion behavior in the electromagnetic field as well as the application of different algorithms of the big data treatment as well as prediction of chemical properties by chemoinformatics approaches. Our primary goal is to develop mass spectrometry platforms and analytical solutions for a wide area of researches.
  • Mobile Robotics Lab (Prof. Gonzalo Ferrer)
    The Mobile Robotics laboratory is focused on robot navigation, and mainly our interests are within Path Planning and Perception and how to combine both into new solutions for robotics. The full autonomy milestone for robots has not arrived yet, and we are slowly closing the gap; however, there are still many hindrances, such as uncertainty, scalability, and reliability.
  • Scientific Computing (Prof. Ivan Oseledets)
    Our research focuses on developing breakthrough numerical techniques for solving a broad range of high-dimensional problems. The key ingredient is the effective decomposition of multidimensional arrays (tensors). Our recent interests also involve graph mining, recommender systems, and topological shape optimization.
  • Wireless Sensing (Prof. Andrey Somov)
    Wireless Sensing Lab research focuses on the design, optimization, and implementation of the intelligent energy-efficient wireless sensors. We conduct research on the low-power wireless sensors running the artificial intelligence onboard and powered by energy harvesting.