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<\/div>\r\n\t\t\tAdvanced Data Analytics in Science and Engineering Group<\/a>Prof.\u00a0Evgeny Burnaev<\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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ADASE<\/strong> group research is about developing efficient machine learning methods<\/strong> and their use for the solution of applied engineering problems and industrial analytics.<\/p>\n

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.<\/p>\n <\/div>\r\n <\/div>\r\n<\/div>

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<\/div>\r\n\t\t\tAdvanced Multiscale Simulations Lab<\/a>Prof.\u00a0Nikolay Brilliantov<\/a>,\r\nProf.\u00a0Alexey Vishnyakov<\/a>,\r\nProf.\u00a0Vladimir Palyulin<\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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The main goal of the lab is developing a novel multiscale modeling<\/strong> paradigm, where the emerging methods of Artificial Intelligence<\/strong> (AI) are incorporated into traditional mathematical modeling techniques, such as finite elements, Molecular Dynamics, Langevin Dynamics, Monte Carlo, and Lattice Boltzmann simulations.<\/p>\n

We explore Complex Systems<\/strong>, such as multiphase Soft Matter<\/strong> (liquids, solutions, complex liquids, porous media, etc.) and Active Matter<\/strong> (self-driven particles, bacteria, animals, pedestrians, vehicles, robotic swarms, drones, etc.). Our primary focus is fundamental academic research that possesses a significant application potential and demand from industry and society.<\/p>\n <\/div>\r\n <\/div>\r\n<\/div>

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<\/div>\r\n\t\t\tApplied Information Theory Group<\/a>Prof.\u00a0Alexey Frolov<\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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The group researches the border of information theory, communications, and machine learning<\/strong> with a particular focus on the application of the research results in communications, including engineering and industry problems.<\/p>\n <\/div>\r\n <\/div>\r\n<\/div>

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<\/div>\r\n\t\t\tArtificial Intelligence in Dynamic Action<\/a>Prof. Pavel Osinenko <\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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The AIDA Lab is dedicated to the study and application of Artificial Intelligence<\/strong> (AI) in dynamical environments<\/strong>, with a special focus on safety and trustworthiness<\/strong>. Among various machine learning approaches, we put a special focus on reinforcement learning<\/strong> – a methodology resembling the action of living beings in changing, uncertain environments that react by punishment and reward. Applied to the human economy, AI has to fulfill requirements on safety and trustworthiness, especially regarding personal data privacy. These requirements become particularly challenging in the dynamical application of AI, such as robotic, autonomous driving, medical therapy support, chemical engineering, energy management, etc.<\/p>\n

The Lab seeks to apply an interdisciplinary approach<\/strong> by the fusion of machine learning<\/strong> with various fields, such as system and control theory, to develop novel dynamic AI methods<\/strong>.<\/p>\n <\/div>\r\n <\/div>\r\n<\/div>

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<\/div>\r\n\t\t\tComputational Imaging<\/a>Prof.\u00a0Dmitry Dylov<\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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The research group has a twofold focus:<\/p>\n

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  1. Fundamental mathematical aspects<\/strong> of image formation<\/strong><\/li>\n
  2. Applied aspects<\/strong> of image processing and analytics<\/strong>.<\/li>\n<\/ol>\n

    The synergy between imaging systems and image interpretation is at the forefront of our scientific interest. The team members rely extensively on modern computer vision and deep learning methods<\/strong>, with frequent forays into adjacent disciplines for motivation and new ideas.<\/p>\n

    Historically, our most impactful discoveries belong to the area of biomedical imaging<\/strong>, with applications in optical microscopy, MRI, X-ray\/CT scanners<\/strong>, and others.\u00a0We have built two laboratories at Skoltech, where students can embed their computational algorithms into the consumer cameras and wearable sensors. The main industrial partner of the group is Philips Labs Rus, located in Technopark, where several students are involved in R&D on a joint educational track.<\/p>\n <\/div>\r\n <\/div>\r\n<\/div>

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    <\/div>\r\n\t\t\tComputational Molecular Science<\/a>Prof.\u00a0Maxim Fedorov<\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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    The primary research interest of the group is in finding new ways in chemical informatics<\/strong> that are based on a combination of physical chemistry methods with machine learning techniques<\/strong> for the prediction of properties of organic compounds<\/strong>.<\/p>\n

    Our primary goal is to develop methods that, on the one hand, are accurate and, on the other hand, are universal and have vast applicability domains.<\/p>\n <\/div>\r\n <\/div>\r\n<\/div>

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    <\/div>\r\n\t\t\tComputer Vision<\/a>Prof.\u00a0Victor Lempitsky<\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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    CV group research is about designing computer systems<\/strong> that can extract, organize, and quantify information contained in images of various types and origins<\/strong>.<\/p>\n

    For this purpose, the group develops new machine learning techniques<\/strong> (deep learning in particular) and optimization techniques<\/strong> that are robust and flexible enough to handle and to adapt to the diversity of image data in the modern world.<\/p>\n <\/div>\r\n <\/div>\r\n<\/div>

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    <\/div>\r\n\t\t\tDigital Agriculture<\/a>Prof.\u00a0Ivan Oseledets<\/a>,\r\nProf.\u00a0Maria Pukalchik<\/a><\/span>\t\t\t<\/span>\r\n\t\t\t
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    Our research group is focused on developing efficient machine learning and AI methods<\/strong> to the broad scope of the environment and agriculture issues<\/strong>. Key directions of our team are:<\/p>\n