summer school
of machine learning
July 14–27

Harbin Institute of Technology (HIT), located in Harbin, China
|SMILES
2025
General
partner
Host
Scientific
partner
school
The Skoltech Summer School of Machine Learning (SMILES-2025) is a 12-day intensive course focused on generative methods in AI. It is organized by Skoltech in collaboration with the Harbin Institute of Technology (HIT), China. The program will take place from July 14 to July 27, 2025.

SMILES-2025 will welcome 100 participants from all over the world attending in person, while up to 300 participants will join online. All attendees will have access to cutting-edge lectures, hands-on workshops, and hackathons designed to tackle real-world industry challenges. The school aims to bring together talented young researchers from Russia and China. One of the key goals of SMILES-2025 is to foster international collaboration and knowledge exchange. Participants will gain practical experience in advanced AI techniques, including generative models, multimodal methods, large language models, multi-agent systems. The school also offers professional networking, career development, mentoring from industry experts, and a cultural excursion for a well-rounded experience.
directions
Large language models

Multimodal approaches

Multi-agent systems

Generative approaches

Self-learning methods

Safe AI
Cost:
no registration fees; the school will provide travel grant* and cover the accommodation
*covering flight expenses to China from major Russian cities (the list is being finalized)
Format:
in-person and online
Working language:
English
Visa:
to travel to China, you need to apply for a visa; standard fee for Russian citizens is 6,300 RUB; the process doesn't take much time and effort, and we will provide assistance if needed
Selection by competition:
is mandatory for both in-person and online participation
venue
The summer school will be held at the Faculty of Computing of the Harbin Institute of Technology (HIT) in Harbin, China.
organizers
Evgeny
Burnaev
Doctor of Sciences in Physics and Mathematics. Professor, Head of the Artificial Intelligence Center, Skoltech
Alexey Zaytsev
Ph.D. in Physics and Mathematics. Head of the LARSS Laboratory at the Artificial Intelligence Center, Skoltech
Petr
Sokerin
Program Coordinator,
Research Engineer,
Skoltech AI Center.
Instructor of professional education programs
Evgeny
Burnaev
Doctor of Sciences in Physics and Mathematics. Professor, Head of the Artificial Intelligence Center, Skoltech
Alexey Zaytsev
Program Coordinator,
Research Engineer,
Skoltech AI Center.
Instructor of professional education programs
Petr
Sokerin
Ph.D. in Physics and Mathematics. Head of the LARSS Laboratory at the Artificial Intelligence Center, Skoltech
speakers
Alexander Korotin
Ph.D. in Physics and Mathematics. Head of the Research Group at the Artificial Intelligence Center, Skoltech. Research Scientist, AIRI
Andrey Kuznetsov
Ph.D. in Technical Sciences. Head of the laboratory of multimodal generative AI, FusionBrain, AIRI. One of the founders of the Kandinsky family of image and video synthesis models
Ph.D. in Physics and Mathematics. Head of RSI group at AIRI and the SAIL MTUCI laboratorty. Research scientist, Skoltech
Oleg
Rogov
Dongjing
Miao
Professor of the HIT University, School of Computer Science and Technology. Research areas: multi-model databases, HTAP database, computational complexity theory and efficient algorithms related to big data computing, energy big data and visualization

Google Scholar
Professor of the HIT University, School of Computer Science and Technology. Research areas: natural language processing, network information processing and application

Google Scholar
Chengjie
Sun
Yuanchao
Liu
Professor of the HIT University, School of Computer Science and Technology. Reviewer of CCF AI top conferences and other academic journals such as ACL, EMNLP, COLING, NIPS. Research areas: machine learning, deep learning, natural language processing, emotional computing, lnowledge mapping, blockchain technology

Google Scholar
Maria
Tikhonova
Ph.D. in Computer Science.
Associate Professor at the Faculty of Computer Science, Higher School of Economics.
Head of the AGI NLP Research Group, SberDevices, Sber
Sergey Barannikov
Ph.D. in Mathematics, University of California – Berkeley. Senior Research Fellow at the Igor Krichever Center for Advanced Studies, Skoltech
Doctor of Sciences in Physics and Mathematics. Professor, Artificial Intelligence Center, Skoltech
Ivan
Tyukin
Egor
Shvetsov
Curator of Project Activities and Lecture Planning, Head of the AI Center Division, Skoltech
Irina Piontkovskaya
Head of the NLP Research Team, RRI
Ph.D. in Computer Science. Associate Professor at the Faculty of Computer Science, Higher School of Economics.
Head of the AGI NLP Research Group, SberDevices, Sber
Maria
Tikhonova
Irina Piontkovskaya
Professor of the HIT University, School of Computer Science and Technology. Research areas: multi-model databases, HTAP database, computational complexity theory and efficient algorithms related to big data computing, energy big data and visualization

Google Scholar
Dongjing
Miao
Ph.D. in Technical Sciences. Head of the laboratory of multimodal generative AI, FusionBrain, AIRI. One of the founders of the Kandinsky family of image and video synthesis models
Andrey Kuznetsov
Oleg
Rogov
Ph.D. in Physics and Mathematics. Head of RSI group at AIRI and the SAIL MTUCI laboratorty. Research scientist, Skoltech
Head of the NLP Research Team, RRI
Ph.D. in Mathematics, University of California – Berkeley. Senior Research Fellow at the Igor Krichever Center for Advanced Studies, Skoltech
Sergey Barannikov
Ivan
Tyukin
Doctor of Sciences in Physics and Mathematics. Professor, the Artificial Intelligence Center, Skoltech
Alexander Korotin
Ph.D. in Physics and Mathematics. Head of the Research Group at the Artificial Intelligence Center, Skoltech. Research Scientist, AIRI
Yuanchao
Liu
Professor of the HIT University, School of Computer Science and Technology. Reviewer of CCF AI top conferences and other academic journals such as ACL, EMNLP, COLING, NIPS. Research areas: machine learning, deep learning, natural language processing, emotional computing, lnowledge mapping, blockchain technology

Google Scholar
Chengjie
Sun
Professor of the HIT University, School of Computer Science and Technology. Research areas: natural language processing, network information processing and application

Google Scholar
The list of speakers is preliminary and subject to change and expansion.
participants
SMILES-2025 is designed for students, graduate students, early-career researchers, and AI enthusiasts from Russia and China who are actively engaged in advancing modern machine learning through innovative research and applications.
selection process
Potential participants are required to submit the following set of documents and complete a small test:

CV
Describing your: education level, participation in scientific work, scientific events and teaching activities. Achievements: published articles, relevant work experience, topics of interest, thesis topics with the place of graduation. ML/DL tool skills and proficiency. 1-2 pages.

Motivation letter;
Up to 1000 words long in which it is necessary to disclose: motivation to participate in school, ML/DL experience, if available, school expectations and future career plans

Test with 10 questions on relevant calculus, statistics, machine learning and deep learning;

Review Presentation: slides (pdf) + video recording (mp4/avi/mov/webm). Video recording is optional but it will give you extra points.

There are three possible options for a review:
  • A description of your own ML/DL research
  • A reproduction of the results of an article from one of the school's topics over the past 5 years. Adding ideas for improvement will be considered an additional plus to your application.
  • A review of at least 3 articles on one of the school's topics

The video should last 5-10 minutes; ensure it captures your face, voice, and the slides. Upload the video to a cloud drive and don't forget to give us access to view it.

Review requirements
Articles for review and reproduction should be from A* rank conferences (NeurIPS, ICML, CVPR, etc).
The presentation should include:
  1. Introduction
  2. Problem statement
  3. Methods
  4. The analytical table
  5. Link to the repository if the article is being reproduced

Requirements for the analytical table:
  • Analysis of trends on the chosen topic over the past 5 years based on articles, patents and open sources (NeurIPS, CVPR, etc.).
  • Comparison of datasets (size, domain, preprocessing) and justification of their choice.
  • Evaluation metrics with an explanation of why some metrics are used more often than others and what problems they may have.
  • Analysis of SOTA models: analysis of efficiency on different tasks, complexity of implementation.
  • Comparison of implementations in repositories (Hugging Face, GitHub, Papers with Code): pros and cons, configuration difficulties.
  • You can use the first table here as an example.
LLM usage policy
Authenticity is crucial

We strongly discourage using large language models (LLMs) to compose your application materials. Instead, write from your heart, in your own voice, to capture your one-of-a-kind perspective and passion. We read hundreds of applications and look for submissions that demonstrate genuine enthusiasm and originality. While LLMs can be tempting, they tend to produce generic content with little value — ultimately reducing your chances of success. Please note that we will employ an LLM detector to verify the integrity of all submitted materials.
posters
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.
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*
  • poster session with the presentation of the results of scientific works by the participants of the summer school
  • the best presentations will be awarded with commemorative prizes
schedule
Each day will consist of three lectures or seminars, followed by time for working on projects and for relaxation. The schedule will be published later.
20/APRIL
Application deadline
20/MAY
Admission lists
14/JULY
START >>>
partners
host
general partner
scientific partner
registration
Upload your CV
pdf format
Upload your motivation letter
pdf format
Upload your presentation
pdf/pptx format
Mp4/mov/avi/webm format. Upload the video to a cloud drive and don't forget to give us access to view it.

Complete the test

In these problems, the answer may be either an integer or a decimal number. If the answer is a decimal, it should be rounded to one decimal place and written using ONLY a dot as the decimal separator. For example, if the result is 3.14159, the correct answer is 3.1.
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previous SMILES
FAQ
contacts
Or via e-mail at
SMILES2025@skoltech.ru
Skoltech Artificial Intelligence Center
e-mail: ai4esg@skoltech.ru
Telegram-channel
Any questions about the summer school
can be asked in the Telegram-channel

Skolkovo Institute of Science and Technology (Skoltech)

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