summer school
of machine learning
20–31 august

Belokurikha Mountain, Altai Krai region
The Skoltech Summer School of Machine Learning (SMILES) is a 12-day intensive course dedicated to modern statistical methods of machine learning. Its objective is to bring together talented Russian youth to explore the issues surrounding artificial intelligence in the unique natural setting of the Altai Krai region, specifically in the mountainous area of Belokurikha. SMILES-2023 will cover topics fundamental to constructing contemporary predictive models in machine learning, incorporating physical and mathematical models of processes to address engineering challenges in sustainable development, ESG risk assessment, and optimization of managerial decisions for risk reduction.
no registration fees
in-person and online
Working language:
Selection by competition:
is mandatory for both in-person and online participation
The summer school will take place against the backdrop of the unique views of Belokurikha Mountain. The event venue is surrounded on all sides by a mountain range. The rocks of whimsical shapes, famous springs, hikes along mountain trails with breathtaking views of the surroundings — these are just a small part of the attractions of this beautiful region.
    Participation in SMILES is open to master's and doctoral students from leading universities in Russia, as well as young researchers actively engaged in research in the field of machine learning and its application for modeling technical and physical systems.
    selection process
    Potential participants are required to submit the following set of documents:
    Motivation letter;
    Brief abstract of results from one of their research projects (not more than 1 page in pdf format excluding the list of references, Times New Roman font, 12/14 pt, 1/1.5 line spacing).

    It is preferable that the research topic is in the field of machine learning and data analysis or their applications, but this requirement is not mandatory.
    Participants who have passed the selection process must prepare and present a poster with the results described in the abstract at the conference. This is a long-standing tradition of summer schools: we want to introduce all school participants to your work. Only with a poster, you will get access to SMILES.
    (PDF) example of a poster made in LATEX
    (PDF) example of a poster made in POWERPOINT
    *The poster topic may not be related to machine learning and data analysis. In this case, the author must also present their thoughts on how machine learning and data analysis could allow for a more efficient solution to the problem.
    Physics-Informed Machine Learning (PIML) is currently developing actively.
      Physical sciences will receive unprecedented acceleration through the merging of modeling tools on HPC clusters and deep learning tools to enhance and accelerate this process.
      One of the prominent researchers in the field of machine learning, Max Welling.
      Related directions in machine learning considered at the summer school
        probabilistic machine learning methods
        machine learning methods with architectural or mathematical constraints for maintaining physical conservation laws and following trajectories of physical processes
        machine learning methods that allow modeling and accounting for the geometric and topological structure of data when building predictive models
        the use of predictive models for modeling physical and technical systems
        training format
        • mandatory lectures on each direction
        • practical seminars mainly focused on demonstrating the operation and programming of the discussed methods and algorithms
        • team formation of 2–4 people and implementation of one of the proposed projects during the hackathon
        • session with presentations by teams on the results of the projects implemented during the summer school*

          *the best presentations will be awarded with commemorative prizes
        • poster session with the presentation of the results of scientific works by the participants of the summer school
        The schedule is preliminary. It is expected to include an excursion program and an increase in duration.
        Evgeny Burnaev
        Professor, Head of the Applied AI Center, Skoltech
        Alexey Zaytsev
        Ph.D., Head of the LARSS Laboratory at the Applied AI Center, Skoltech
        Vladimir Vanovsky
        Head of the Research Group at the Applied AI Center, Skoltech, Associate Professor at the Department of General Physics at the Moscow Institute of Physics and Technology (MIPT)
        Irina Gayda
        MBA, Deputy Director of the Project Center for Energy Transition, Skoltech, Independent Board Member of NOVATEK
        Alexander Bernshtein
        Ph.D., Professor at the Applied AI Center, Skoltech
        Dmitry Vetrov
        Ph.D., Professor at the National Research University Higher School of Economics (HSE)
        Ilya Trofimov
        Ph.D., Research Scientist, Skoltech
        Alexander Korotin
        Researcher at Skoltech
        Nikita Lazarevich
        Expert at the Applied AI Center, Skoltech
        Alexander Bulkin
        Senior Research Engineer at the Applied AI Center, Skoltech
        Alexander Lukashevich
        Research Engineer at the Applied AI Center, Skoltech
        The list of speakers is preliminary and subject to change and expansion.
        Application deadline
        Admission lists
        START >>>
        registration is closed
        Any questions about the summer school can be asked in the Telegram-channel
        or via e-mail at
        Skoltech Applied AI Center

        Skolkovo Institute of Science and Technology (Skoltech)

        Bolshoy Boulevard 30, bld.1

        Skolkovo Innovation Center Territory