andrzejcichocki



Personal Websites

Andrzej Stanislaw Cichocki

Professor Andrzej Cichocki graduated from the Warsaw University of Technology, Poland, where he obtained his PhD and Doctor of Science degree (Habilitation) in Electrical Engineering and Computer Science  and received prestigious Alexander Humboldt  and DFG Fellowships  in Germany in 1984-1994.

Currently he is working  in the area of artificial intelligence and biomedical applications of advanced data analytic technologies. Andrzej served as a Senior Team Leader and Head of the Laboratory for Advanced Brain Signal Processing at RIKEN Brain Science Institute, the leading research organization in Japan and also holds a position of Visiting Professor in universities in Poland, Japan, China and in the Systems Research Institute of the Polish Academy of Science.

Professor Cichocki is a holder of numerous patents and software developments and author of  highly cited and well-known publications and 6 monographs in English. According  to Google Scholar he has h-index 97 with more than 46,000 citations. Dr. Cichocki is a winner of numerous awards and prizes for his scientific achievements, awards for best conference presentations and best journal publications, e.g., recently  presented  paper about tensors published in the  IEEE Signal Processing Magazine  (2019) and  in Entropy about alpha-beta divergences (2014 and 2015). He is one of the most cited Polish scientist in area of AI, Computer Science and Engineering.

Under the guidance of Professor Cichocki the new Laboratory “Tensor Networks and Deep Learning for Applications in Data Mining”  is established at SKOLTECH. It won the fifth competition for the Russian Federation Government grants for state support in 2017-2021 of scientific research conducted under the supervision of leading scientists at Russian universities and scientific organizations (“Megagrant”) with the total funding 90 million Rubles. The mission of the Laboratory is to perform cutting-edge opened research in the design and analysis of deep neural networks, tensor decomposition, tensor networks and multiway component analysis for  biomedical applications and improving quality of life. He is Fellow of IEEE from 2013.

His research focus on

1. AI, especially Deep Neural Networks, Unsupervised and Supervised Algorithms

2. Tensor Networks and Tensor Decomposition for  Machine Learning and Big Data

3. EEG Brain Computer Interfaces, Human Computer Interactions and Human Cooperation

4. Signal/Image Processing and  Machine Learning Algorithms

5. Portfolio Optimization and Time Series Analysis

6. Time Series Forecasting Using Deep Neural Networks and Machine Learning

7. Humanoid Robotics and Human Robot Interactions

Selected List of publications in 2016-2020 with  the  SKOLTECH affiliation

Peer-revived Journal Publications (Web of Science)

2020

  1.  A.-H. Phan, A. Cichocki, I. Oseledets, S. A. Asl, G.Calvi, and D. Mandic, “Tensor networks for latent variable analysis: Higher order Canonical Polyadic decomposition”, IEEE Transaction on Neural Network and Learning System, https://doi.org/10.1109/TNNLS.2019.2929063 (early access), 2020 (IF=11.68).
  1.  A.-H. Phan, A. Cichocki, A. Uschmajew, P. Tichavský, G. Luta, D. Mandic, “Tensor networks for latent variable analysis: Algorithms for tensor train decomposition”, IEEE Transaction on Neural Network and Learning System, (accepted for publication in Nov. 2020 )(IF=11.68).
  1. Sidney R. Lehky, Anh-Huy Phan, Andrzej Cichocki, and Keiji Tanaka, “Face representations via Tensorfaces of various complexities”, Neural Computation, 2020, pp. 1-49. (IF=5.54).
  2. F. Sedighin,  A. Cichocki, T. Yokota and Q. Shi,  “Matrix and Tensor Completion in Multiway Delay Embedded Space Using Tensor Train, with Application to Signal Reconstruction,” in IEEE Signal Processing Letters, vol. 27, pp. 810-814, 2020, doi: 10.1109/LSP.2020.2990313.
  3.  Appriou, A., Cichocki, A., & Lotte, F. (2020). “Modern machine learning algorithms to classify cognitive and affective states from electroencephalography signals”. IEEE Systems, Man and Cybernetics Magazine.
  4. Liu Q, Jiao Y, Miao Y, Zuo C, Wang X, Cichocki A, Jin J. Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA. Neurocomputing. 2020 Feb 22;378:36-44.
  5. Qiu, Y., Zhou, G., Zhang, Y. and Cichocki, A., 2020. Canonical polyadic decomposition (CPD) of big tensors with low multilinear rank. Multimedia Tools and Applications, pp.1-21.
  6. Duan, F., Huang, Z., Sun, Z., Zhang, Y., Zhao, Q., Cichocki, A., Yang, Z. and Solé-Casals, J., 2020. Topological Network Analysis of Early Alzheimer’s Disease Based on Resting-State EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 
  7. Zuo C, Jin J, Yin E, Saab R, Miao Y, Wang X, Hu D, Cichocki A. Novel hybrid brain–computer interface system based on motor imagery and P300. Cognitive Neurodynamics. 2020 Apr;14(2):253-65.
  8. Chen Z, Jin J, Daly I, Zuo C, Wang X, Cichocki A. Effects of Visual Attention on Tactile P300 BCI. Computational Intelligence and Neuroscience. 2020 Feb 19;2020.
  9. Akter MS, Islam MR, Iimura Y, Sugano H, Fukumori K, Wang D, Tanaka T, Cichocki A. Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG. Scientific Reports. 2020 Apr 27;10(1):1-7.
  10.  Li S, Jin J, Daly I, Zuo C, Wang X, Cichocki A. Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns. Frontiers in Neuroscience. 2020 Jan 31;14:54.
  11. Phan, A. H., Tichavský, P., & Cichocki, A. (2019). Error preserving correction: A method for CP decomposition at a target error bound. IEEE Transactions on Signal Processing, 67(5), 1175-1190. (IF=4.3).  
  12. Wu, Q., Zhang, Y., Liu, J., Sun, J., Cichocki, A., & Gao, F. (2019). “Regularized    group sparse discriminant analysis for P300-Based Brain-Computer Interface”. International Journal of Neural Systems, (IF=6.507). https://www.worldscientific.com/doi/abs/10.1142/S0129065719500023
  13. Zhang, Z., Duan, F., Solé-Casals, J., Dinarès-Ferran, J., Cichocki, A., Yang, Z., & Sun, Z. (2019). “A Novel deep learning approach with data augmentation to classify motor imagery signals”. IEEE Access, 7, 15945-15954. (IF=4.098) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8630915
  14. Guo, M., Jin, J., Jiao, Y., Wang, X., & Cichocki, A. (2019). “Investigation of visual stimulus with various colors and the layout for the oddball paradigm in ERP-based BCI”. Frontiers in Computational Neuroscience, 13, 24. https://www.frontiersin.org/articles/10.3389/fncom.2019.00024/full
  15. Jiao, Y., Zhang, Y., Chen, X., Yin, E., Jin, J., Wang, X., & Cichocki, A. (2019). “Sparse group representation model for motor imagery EEG classification”. IEEE Journal of Biomedical and Health Informatics, 23(2), 631-64. https://ieeexplore.ieee.org/abstract/document/8353425
  16. Cheng, J., Jin, J., Daly, I., Zhang, Y., Wang, B., Wang, X., & Cichocki, A. (2019). “Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling”. Biomedical Engineering/Biomedizinische Technik, 64(1), 29-38. https://www.degruyter.com/view/j/bmte.2019.64.issue-1/bmt-2017-0082/bmt-2017-0082.xml 
  17. Zhu, L., Cui, G., Cao, J., Cichocki, A., Zhang, J., & Zhou, C. (2019). “A hybrid system for distinguishing between brain death and coma using diverse EEG features”. Sensors, 19(6), 1342. https://www.mdpi.com/1424-8220/19/6/1342 
  18. Zheng, W. L., Liu, W., Lu, Y., Lu, B. L., & Cichocki, A. (2019). “EmotionMeter: A multimodal framework for recognizing human emotions”. IEEE Transactions on Cybernetics, (49) Issue 2, pp. 631-641. Journal impact factor: (IF=8.8). https://www.researchgate.net/profile/Wei_Long_Zheng/publication/322998203_EmotionMeter_A_Multimodal_Framework_for_Recognizing_Human_Emotions/links/5c8d836d45851564fae181ae/EmotionMeter-A-Multimodal-Framework-for-Recognizing-Human-Emotions.pdf  
  19. Jin, J., Miao, Y., Daly, I., Zuo, C., Hu, D., & Cichocki, A. (2019), “Correlation-based channel selection and regularized feature optimization for MI-based BCI”. Neural Networks, 118, 262-270.
  20. Jin, J., Li, S., Daly, I., Miao, Y., Liu, C., Wang, X., & Cichocki, A. (2019). “The study of generic model set for reducing calibration time in P300-based Brain-Computer Interface”. IEEE Transactions on Neural Systems and Rehabilitation Engineering (in print) (IF=3.972).
  21. Liu, Q., Jiao, Y., Miao, Y., Zuo, C., Wang, X., Cichocki, A., & Jin, J. (2019), “Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA”. Neurocomputinghttps://www.sciencedirect.com/science/article/abs/pii/S0925231219314286#!
  22. Zuo, Cili, Jing Jin, Erwei Yin, Rami Saab, Yangyang Miao, Xingyu Wang, Dewen Hu, and Andrzej Cichocki. “Novel hybrid brain–computer interface system based on motor imagery and P300.” Cognitive Neurodynamics (2019): 1-13.

2018

25. Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018). “A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update”. Journal of Neural Engineering, 15(3), 031005. Journal impact factor: 2.94.  https://hal.inria.fr/hal-01846433/document

26. Zhang, Y., Wang, Y., Zhou, G., Jin, J., Wang, B., Wang, X., & Cichocki, A. (2018). “Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces”. Expert Systems with Applications, 96, 302-310. Journal impact factor: 4.68.

18. Lee, N., & Cichocki, A. (2018). “Fundamental tensor operations for large-scale data analysis using tensor network formats”. Multidimensional Systems and Signal Processing, 29(3), 921-960. Journal impact factor: 1.37. https://link.springer.com/article/10.1007/s11045-017-0481-0

27. Xu, X., Wu, Q., Wang, S., Liu, J., Sun, J., & Cichocki, A. (2018). “Whole brain fMRI pattern analysis based on tensor neural network”. IEEE Access. Journal impact factor 3.57.  https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8319500

28. Martín-Clemente, R., Olias, J., Thiyam, D. B., Cichocki, A., & Cruces, S. (2018). “Information theoretic approaches for motor-imagery BCI systems: Review and experimental comparison”. Entropy, 20(1), 7.  https://www.mdpi.com/1099-4300/20/1/7

29. Elgendi, M., Kumar, P., Barbic, S., Howard, N., Abbott, D., & Cichocki, A. (2018). “Subliminal priming–state of the art and future perspectives”. Behavioral Sciences (Basel, Switzerland), 8(6). Journal impact factor: 2.61. https://www.mdpi.com/2076-328X/8/6/54

30. Cao, K. Wang, G. Han, J. Yao, and A. Cichocki (2018) “A Robust PCA approach with noise structure learning and spatial-spectral low-rank modeling for Hyperspectral Image Restoration” (2018)  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Journal, Volume: 11 , Issue: 10 , pp 3863 – 3879   Oct. 2018,  impact factor 2.78.  https://ieeexplore.ieee.org/abstract/document/8454794 

31. Yu, G. Zhou, A. Cichocki, S. Xie: Learning the hierarchical parts of objects by deep non-smooth nonnegative matrix factorization. IEEE Access 6: 58096-58105 (2018). Impact factor 3.357. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8481457

32. Jurica, P., Struzik, Z.R., Li, J., Horiuchi, M., Hiroyama, S., Takahara, Y., Nishitomi, K., Ogawa, K. and Cichocki, A., 2018. “Combining behavior and EEG analysis for exploration of dynamic effects of ADHD treatment in animal models”. Journal of Neuroscience Methods, 298, pp.24-32, (impact factor: 2.94). https://www.sciencedirect.com/science/article/pii/S0165027018300104#!

33. Lin, J.W., Chen, W., Shen, C.P., Chiu, M.J., Kao, Y.H., Lai, F., Zhao, Q. and Cichocki, A., 2018. “Visualization and sonification of long-term epilepsy electroencephalogram monitoring”. Journal of Medical and Biological Engineering, pp.1-10.)  https://link.springer.com/article/10.1007%2Fs40846-017-0358-6

34. Qiu, G.Zhou, Q. Zhao, A. Cichocki, “Comparative study on the classification methods for breast cancer diagnosis”, Bulletin of Polish Academy of Sciences Technical Sciences 66(4),  pp. 841-849, (2018) DOI: 10.24425/bpas.2018.125931.  http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-1c49c94f-8923-44d1-bf09-2559e673b642

35. Sole-Casals, J., Caiafa, C. F., Zhao, Q., & Cichocki, A. “Brain-Computer Interface with corrupted EEG data: A Tensor Completion Approach”.  Cognitive Computation (2018), (accepted) https://doi.org/10.1007/s12559-018-9574-9arXiv preprint arXiv:1806.05017. Impact Factor 4.79. https://link.springer.com/article/10.1007/s12559-018-9574-9

36. V Osin, Cichocki and E. Burnaev: “Fast multispectral deep fusion networks”, Bulletin of Polish Academy of Sciences Technical Sciences   66(4), pp 875-889  (2018), DOI: 10.24425/bpas.2018.125935.

2017

37. Cichocki,  A-H. Phan,  Q. Zhao, ,  N. Lee, I.V. Oseledets, M. Sugiyama, D. Mandic, Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Potential Applications and Perspectives,  Foundation and Trends in Machine Learning Monograph,  9, no. 6 (2017): 431-673.)https://arxiv.org/pdf/1708.09165.pdf

38. A. Cichocki, N. Lee, I.V. Oseledets, A-H. Phan, Q. Zhao, D. Mandic, “Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 1 Low-Rank Tensor Decompositions”, Vol. 9, No. 4-5, 249-429, Foundation and Trends in Machine Learning (January 2017).  https://arxiv.org/pdf/1609.00893.pdf

39.Jin, H. Zhang, I. Daly, X. Wang, A. Cichocki , “An improved P300 pattern in BCI to catch user’s attention”. Journal of Neural Engineering , Vol. 14, No. 3, (2017). Impact factor 3.92

40. Li, C. Li, A. Cichocki, “Canonical Polyadic decomposition with auxiliary information for Brain-Computer Interface”, IEEE J Biomed Health Information 2017, 21(1):263-271. Impact Factor: IF=3.45.

41. B. Thiyam, S. Cruces, J. Olias, A. Cichocki, (2017) “Optimization of Alpha-Beta Log-Det Divergences and their application in the spatial filtering of two class motor imagery movements”, Entropy 2017, 19(3), 89. http://www.mdpi.com/1099-4300/19/3/89

42. Deshpande, G., Rangaprakash, D., Oeding, L., Cichocki, A., & Hu, X. P. (2017). “A New generation of brain-computer interfaces driven by discovery of latent EEG-fMRI linkages using tensor decomposition”. Frontiers in Neuroscience, 2017, 11, 246. IF=3.42. https://www.frontiersin.org/articles/10.3389/fnins.2017.00246/full

43. Tichavský, P., Phan, A. H., & Cichocki, A. (2017). “Non-orthogonal tensor diagonalization”. Signal Processing, 138, 313-320. Impact Factor: IF=4.7 http://www.sciencedirect.com/science/article/pii/S0165168417301299

44. Che, M., Cichocki, A., & Wei, Y. (2017). „Neural networks for computing best rank-one approximations of tensors and its applications”. Neurocomputing, 267,6, pp. 114-133. http://www.sciencedirect.com/science/article/pii/S0925231217308263 Impact factor is 3.211.

45. Li, Y., Wang, F., Chen, Y., Cichocki, A., & Sejnowski, T. (2017). The effects of audiovisual inputs on solving the cocktail party problem in the human brain: An fMRI study. Cerebral Cortex, 28(10), 3623-3637. https://www.researchgate.net/profile/Fangyi_Wang/publication/320413698_The_Effects_of_Audiovisual_Inputs_on_Solving_the_Cocktail_Party_Problem_in_the_Human_Brain_An_fMRI_Study/links/5bd136a5a6fdcc6f79003d49/The-Effects-of-Audiovisual-Inputs-on-Solving-the-Cocktail-Party-Problem-in-the-Human-Brain-An-fMRI-Study.pdf

46. Zhou, Q. Zhao, Y. Zhang, T. Adali, S. Xie, A. Cichocki, “Linked component analysis from matrices to high order tensors: Applications to biomedical data”, Proceedings of the IEEE, 104(2): 310-331, 2017. (IF: 5.629) http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7373530

47. L. Chen, J. Jin, I. Daly, Y. Zhang, X. Wang, A. Cichocki. “Exploring combinations of different color and facial expression stimuli for gaze-independent BCIs”. Frontiers in Computational Neuroscience, 2016, 10: Article 5. (IF: 2.653) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731496/pdf/fncom-10-00005.pdf

48. Wang, H., Zhang, Y., Waytowich, N. R., Krusienski, D. J., Zhou, G., Jin, J, Cichocki, A. (2016). „Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(5), 532-541. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7389413

49. Y. Zhang, G. Zhou, Q. Zhao, X. Wang, A. Cichocki, “Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation”, Neurocomputing, 198: 148-154, 2016. (IF: 2.392) http://ac.els-cdn.com/S0925231216003222/1-s2.0-S0925231216003222-main.pdf?_tid=6bfa7fbe-ed09-11e5-b00a-00000aacb35f&acdnat=1458306222_8355756531673539d94c322111068ddb

50. Xie, Z. He, A. Cichocki, X. Fang “Rate of Convergence of the FOCUSS Algorithm”, IEEE Transaction on Neural Networks and Learning Systems (2016). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7423792

51. Zeng, Z., Cichocki, A., Cheng, L., Xia, Y., & Hu, X.. Special Issue on Neurodynamic Systems for Optimization and Applications. IEEE Transactions on Neural Networks and Learning Systems, 27(2), 210-213. (2016) http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7384894

52. Baumert, M.; Porta, A.; Cichocki, A. “Editorial Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications” Proceedings of the IEEE, (2016) http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7386801

Research Monographs (Books)

  • Cichocki A., A-H. Phan,  Q. Zhao, ,  N. Lee, I.V. Oseledets, M. Sugiyama, D. Mandic, “Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Potential Applications and Perspectives”,  Foundation and Trends in Machine Learning  9.6 (2017): 431-673. (May 2017). https://arxiv.org/pdf/1708.09165.pdf
  • Cichocki A.,  N. Lee, I.V. Oseledets,  A-H. Phan,  Q. Zhao,  D. Mandic, “Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 1 Low-Rank Tensor Decompositions”,  Vol. 9, No. 4-5, 249-429, Foundation and Trends in Machine Learning (January 2017). https://arxiv.org/pdf/1609.00893.pdf
  • Cichocki and R. Unbehauen Neural Networks for Optimizations and Signal Processing (Extended edition), New York: Wiley, Nov. 1994.
  • Unbehauen and A. Cichocki: CMOS Switched-Capacitor and Continuous-Time Integrated Circuits and Systems (Springer-Verlag, 1989).

Recent presentations on  international conferences:

  1. Shi,. J.Yin,. J Cai,  A.Cichocki,  T. Yokota,.  L. Chen,. M.Yuan,  and  Jia Zeng “Block Hankel tensor ARIMA for multiple short time series forecasting”, accepted for presentation in the AAAI- 20  conference on AI in New York, A* ranked conference in AI Feb 2020.
  2. Zhao, Q., Sugiyama, M., Yuan, L. and Cichocki, A., 2019, May. Learning Efficient Tensor Representations with Ring-structured Networks. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8608-8612).
  3. Peng, Y., Tang, R., Kong, W., Zhang, J., Nie, F., & Cichocki, A. (2019, May). Joint Structured Graph Learning and Clustering Based on Concept Factorization. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3162-3166). IEEE.
  4. Peng, Y., Long, Y., Qin, F., Kong, W., Nie, F., & Cichocki, A. (2019, May). Flexible Non-negative Matrix Factorization with Adaptively Learned Graph Regularization. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3107-3111)
  5. Phan, A. H., Tichavský, P., & Cichocki, A. (2017, February). Blind source separation of single channel mixture using tensorization and tensor diagonalization. In International Conference on Latent Variable Analysis and Signal Separation (pp. 36-46). Springer, Cham.

Awarded IEEE Fellow in 2013.

Received doktorat honoris causae in Nicolaus Copernicus University (UMK), Torun, Poland in 2017.

The best paper award in  2019 in IEEE Signal Processing Magazine
for the paper “Tensor decompositions for signal processing applications: From two-way to multiway component analysis”, coauthored by A. Cichocki, D. Mandic, L De Lathauwer, A.H. Phan,  Q. Zhao,  C. Caiafa, G, Zhou.

The best paper award in Journal Entropy in 2015
for the paper “Generalized Alpha-Beta divergences and their application to robust non-negative matrix factorization” Entropy 2011, 13(1), 134–170; coauthored by  A. Cichocki, S. Cruces and S. Amari doi:10.3390/e13010134 http://www.mdpi.com/1099-4300/17/2/882/htm

The Best paper award in Journal Entropy for 2014 for the paper coauthored by Andrzej Cichocki and Shun-ichi Amari,
“Families of Alpha- Beta- and Gamma- Divergences: Flexible and robust measures of similarities”

http://www.mdpi.com/about/announcements/521

APNNA Best Paper Award for the paper coauthored by Yunjun Nam, Qibin Zhao, Andrzej Cichocki, and Seungjin Choi “A tongue-machine interface: Detection of tongue positions by glossokinetic potentials,” in Proceedings of the International Conference on Neural Information Processing (ICONIP-2010), Sydney, Australia, November 22-25, 2010.

Excellent ICONIP Paper Award 2016 for the paper couthored by Namgil Lee, Anh-Huy Phan, Fengyu Cong, Andrzej Cichocki. “Nonnegative tensor train decompositions for multi-domain feature extraction and clustering”

Stanislav Abukhovich
PhD Student
gyamfuaasantemensah
Gyamfua Asante-Mensah
PhD student
dmitriiermilov
Dmitry Ermilov
PhD student
juliagusak
Julia (Yulia) Gusak
Research Scientist
konstantinsobolev
Konstantin Sobolev
PhD Student
konstantinsozykin
Konstantin Sozykin
PhD student
andreiznobishchev
Andrei Znobishchev
PhD student
talgatdaulbaev
Talgat Daulbaev
PhD student

ФИО:  Чихоцкий Анджей Станислав

Занимаемая должность (должности): Профессор

Преподаваемые дисциплины: –

Ученая степень: Ph.D в области электротехники и информатики – 1976;  доктор наук  в области электротехники и информатики – 1982,  Варшавский политехнический университет

Ученое звание: нет

Наименование направления подготовки и/или специальности: Электротехника и информатика

Данные о повышении квалификации и/или профессиональной переподготовке: нет

Общий стаж работы: Более 44 лет

Стаж работы по специальности: 44 года