At the Core of YSDA
2000
graduates of the AI Master’s program
60+
courses across CS fields
-
unique сurriculum
Developed by leading IT experts. Programs are continuously updated to reflect the latest scientific advances and real-world practices -
solid theoretical foundation
Built on mathematics and algorithms -
free of charge
All programs are free. Short-term formats are available via open registration; long-term tracks require competitive selection
LLM Scaling
ML
Explore how LLMs are trained on large GPU clusters, and work with scaling techniques, optimizations, and acceleration strategies
CV Week
ML
Generative and visual models used in modern imaging systems: architectures, pipelines, and core techniques
Dmitry Baranchuk
Visual Generative AI Researcher, PhD
- Lead of Visual GenAI team at Yandex Research
- Research internships at ETH Zurich and Meta AI
Roman Isachenko
AI Research Lead, PhD
- Head of Image Analysis Group at Yandex Research
- Lecturer at MSU
Roman Gorb
LLM Systems Expert
- LLM Inference Acceleration Lead at AliceAI
- Former Research Engineer at Huawei Research & Innovation
Kirill Khrylchenko
Recommendation Systems Expert
- Research Scientist and Senior Lecturer at HSE University
- Former R&D Team Lead at Yandex
Nikita Kazeev
Research Fellow in Scientific ML, PhD
- Research Fellow at NUS
- Author of ICML 2025 and npj Computational Materials papers
Ivan Rubachev
Deep Learning for Tabular Data Researcher, PhD
- Researcher at Yandex Research
- Author of RTDL framework (1500+ citations), 8 papers at NeurIPS, ICML, ICLR (3000+ citations)
Stepan Platinskii
Extreme Value Theory & Statistical ML Researcher, MSc
- Researcher at KAUST and ML Developer at Yandex Search
- Author of «Univariate modeling of multivariate precipitation extremes using the block quantiles method» article (Extremes journal, 2025)
Emeli Dral
MLOps & Data Science Leader, MSc
- Co-founder and CTO at Evidently AI
- Lecturer at Harbour Space University (Barcelona)
Clean Shore Project
Together with the Center for Technologies and Society Yandex Cloud, regional researchers, and environmental field teams, YSDA students built a neural network to detect and classify coastal litter using drone imagery. The system identifies object types — from plastic waste to fishing nets — and maps their locations to support cleanup efforts. Already used in nature reserves like Kronotsky, it helps scientists and volunteers plan operations more efficiently and is available as an open-source tool for use worldwide.
Spina Bifida Project
In collaboration with medical researchers and the Center for Technologies and Society Yandex Cloud, YSDA students developed an AI-assisted system for early detection of spina bifida in ultrasound images. Using deep learning models trained on a rare clinical dataset, the tool highlights potential anomalies and assists doctors in identifying the condition during the first trimester of pregnancy — when early intervention is most effective.
IRBIS Project
YSDA students teamed up with the Center for Technologies and Society Yandex Cloud and wildlife researchers to create a neural network that identifies snow leopards in camera trap footage. Trained on tens of thousands of images and video frames, the model filters out irrelevant shots and detects animals in seconds — reducing weeks of manual work for researchers. The system is deployed via a cloud-based app and will soon be able to recognize individual leopards and track their movements to support conservation efforts.
