Skoltech is an international graduate research-focused university that was founded by the group of world-renowned scientists in 2011. Skoltech's curriculum focuses on technology and innovation, offering Master's programs in 11 technological disciplines. Students receive rigorous theoretical and practical training, design their own research projects, participate in internships and gain entrepreneurial skills in English. The faculty is comprised of current researchers with international accreditation and achievements.

Skoltech professor Oseledets named to Russian Presidential Council for Science and Education

Skoltech professor Ivan Oseledets has been appointed to the Russian Presidential Council for Science and Education. President Vladimir Putin signed a decree to this effect on April 14, 2020. The Council is chaired by President Putin.


Ivan Oseledets: “It is highly gratifying that Skoltech is represented once again in the Presidential Council for Science and Education. It is a great privilege for me to sit on the Council. I will do my best to live up to the honor.”

Previously, Skoltech was represented in the Council by professor Artem Oganov.

Ivan Oseledets is an associate professor and Head of the Scientific Computing Group at the Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE). Oseledets is a winner of the Russian President’s Prize in Science and Innovation for Young Scientists.

Doctor of physics and mathematics Oseledets laid the groundwork for a new area in applied mathematics – computational tensor methods – that enable effective processing of multidimensional data arrays where the object under study is presented by tensors. Multidimensional arrays appear in various applied tasks in physics, chemistry, biology, and data processing.

The proposed approach was embodied in Skoltech’s TT-Toolbox, an open software package widely used by scientists around the globe. Research teams at Skoltech and beyond used TT-Toolbox to create a wealth of new efficient algorithms for a broad range of applied problems in chemistry (vibrational states and density-functional equations), physics (magnetic structure of matter), biology (basic kinetic equation), mechanics (mechanical system models), and data analysis (neural network compression). The new algorithms deal with all these tasks several times faster than conventional approaches, without compromising accuracy.

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