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Evgeny Osipov
Evgeny Osipov

Evgeny Osipov

Luleå tekniska universitet
Institutionen för system- och rymdteknik
A3313 Luleå

General Information

Dr. Evgeny Osipov is a full professor in Dependable Communication and Computation Systems. For more detailed information about professional activities please refer to his personal public web-page.

Research Interests

His main research interests are within the area of wireless networking, bio-inspired approach to machine-to-machine communications and communicating systems in general.


  • (2005) PhD in Computer Science (predicate “Cum Laude”). University of Basel, Department of Computer Science, Basel, Switzerland.
  • (2003) Licentiate of Technology in Telecommunications. KTH, Royal Institute of Technology, Department of Microelectronics and Information Technology, Stockholm, Sweden.
  • (1999) Pre-doctoral school in Communication Systems. EPFL, Swiss Federal Institute of Technology,  Pre-doctoral school, Lausanne, Switzerland.
  • (1998) Engineer (degree with Honors). KGTU, Krasnoyarsk StateTechnical University, Faculty of Computer Science, Krasnoyarsk, Russia.


Artikel i tidskrift

Vector Semiotic Model for Visual Question Answering (2022)

Kovalev. A, Shaban. M, Osipov. E, Panov. A
Cognitive Systems Research, Vol. 71, s. 52-63

Compressed Superposition of Neural Networks for Deep Learning in Edge Computing (2021)

Zeman. M, Osipov. E, Bosnić. Z
Ingår i: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings, IEEE, 2021
Artikel i tidskrift

Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks (2021)

Kleyko. D, Kheffache. M, Frady. E, Wiklund. U, Osipov. E
IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, nr. 8, s. 3777-3783

HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing Enabled Embedding of n-gram Statistics (2021)

Alonso. P, Shridhar. K, Kleyko. D, Osipov. E, Liwicki. M
Ingår i: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings, IEEE, 2021

Learning Rule Optimization and Comparative Evaluation of Accelerated Self-Organizing Maps for Industrial Applications (2021)

Gayathri. M, Ariyaratne. A, Kahawala. S, Silva. D, Alahakoon. D, Nanayakkara. V, et al.
Ingår i: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2021