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A good start for international cooperation marked with Memorandum of Understanding





Today a Memorandum of Understanding was signed between the Institute of Informatics and Automation Problems of the National Academy of Sciences of RA, on behalf of Deputy Director of Science Vladimir Sahakyan and the United Institute of Informatics Problems of the National Academy of Sciences of Belarus (http://uiip.bas -net.by/eng/), on behalf of Vice President for Scientific and Innovative Affairs, Sergey Kruglykov.

The aim of the Memorandum is to ensure the development of science and technology in both countries, to strengthen scientific ties through international cooperation.

According to the MOU, both institutions are seeking ways to develop and carry out collaborative activities, such as exchange of scientists and researchers, implementing joint projects in different spheres with the use of technology, development of joint initiatives on information technologies.

The signing of the Memorandum was preceded by a multilateral discussion between the parties, for which besides Vice President for Scientific and Innovative Affairs of the United Institute of Informatics Problems of the National Academy of Sciences of Belarus Sergey Kruglykov, the Director of the Institute of Mathematics of the National Academy of Sciences of Belarus, Sergei Lemeshevsky, and the scientific researcher of the Unified Institute of Mechanical Engineering of the National Academy of Sciences of Belarus Alexander Zaytsev also visited Armenia.

 
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On February 16, 2026 at 12:30 the general seminar of IIAP will be held (the seminar's room, IV floor).

On the agenda there is the report of Dr. Constantine Dovrolis on "Toward Neuro-Inspired AI: Sparse Data, Modular Networks, and Stream-Based Continual Learning".




The defense will take place during the meeting of the Specialized Council 037 “Informatics” at the Institute for Informatics and Automation Problems of NAS of RA on 17 March 2026 at 14.00. (See Abstract:  Edvard Khalafyan –
Entropy‐Driven AI: Probabilistic Inference, Causal Representations, and Adaptive ModelFine‐Tuning ).




                                                           
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