About us
Yandex Research is a team of scientists focusing on fundamental problems in machine learning.
What we do
We study computer vision and self-driving cars, natural language processing and speech technologies, search and recommendation systems. Our research helps to improve the technologies in Yandex products. We also publish our findings at leading machine learning conferences and contribute to the community by organizing competitions, workshops and tutorials.
How we support the community
A creative and collaborative scientific environment is essential for productive research in most fields. We strive to develop the Russian academic community through collaboration with students and universities. For instance, we launched a machine learning residency program to provide an opportunity to grow for early-career researchers. Also, in collaboration with scientists from world-leading universities, we organize projects that move machine learning forward.
Research Areas
Computer vision
Yandex Research team regularly contributes to the computer vision research community, mostly in the field of image retrieval and generative modelling.32 publications4 posts1 datasetNatural language processing
Language is one of the key forms of communication. We study methods of language representation and understanding to simplify human-computer interactions.21 publication2 posts2 datasetsLarge-scale machine learning
Today, training most powerful models often takes significant resources. Our research aims to make large-scale training more efficient and accessible to the entire machine learning community.8 publications1 postMachine learning theory
We study various aspects related to theoretical understanding of ML models and algorithms.24 publications2 postsGraph machine learning
Graphs are a natural way to represent data from various domains such as social networks, molecules, text, code, etc. We develop and analyze algorithms for graph-structured data.11 publications3 posts1 datasetProbabilistic machine learning
Probabilistic machine learning describes methods which enable reasoning and inference over unknown quantities. Commonly used in generative modelling, regression and uncertainty quantification.6 publications
