Personal Websites

Andrzej Cichocki

Professor Andrzej Cichocki graduated from the Warsaw University of Technology, Poland, where he obtained his Doctor of Science degree (Habilitation) in Electrical Engineering and Computer Science in 1982 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 analytics technologies and has a wide international recognition. Prof Cichocki 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 several universities in 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 frequently cited and well-known publications and 6 monographs in English. According to Scopus, he has h-index 52 with more than 11300 citations and according to Google Scholar he has h-index 83 with more than 36000 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 work in the ICONIP (2015) and  journal papers published in Entropy (2014 and 2015).

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 2017-2019 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 research in the design and analysis of deep neural networks, tensor decomposition , tensor networks and multiway component analysis with many biomedical applications.

His research focus on

1. Deep Neural Networks and Unsupervised and Supervised Algorithms

2. Tensor Networks and Tensor Decompositions for Dimensionality Reduction

3. Brain Machine Interfaces

4. Fast Machine Learning Algorithms

5. Portfolio Optimization and Time Series Analysis

Selected List of publications in 2016-2018 with SKOLTECH affilation

Peer-revived Journal Publications (Web of Science)

1. 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.

2. Jiao, Y., Zhang, Y., Chen, X., Yin, E., Jin, J., Wang, X., & Cichocki, A. (2018). “Sparse group representation model for motor imagery EEG classification”. IEEE Journal of Biomedical and Health Informatics. Journal impact factor: 3.45.

3. 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.

4. 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.

5. 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: 4.02.

6. Zheng, W. L., Liu, W., Lu, Y., Lu, B. L., & Cichocki, A. (2018). “EmotionMeter: A multimodal framework for recognizing human emotions”. IEEE Transactions on Cybernetics, (99), 1-13. Journal impact factor: 2.91.

7. 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. Journal impact factor: 1.82.

8. 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

9. Y. Qiu, G.Zhou, Q. Zhao, A. Cichocki, “Comparative study on the classification methods for breast cancer diagnosis”, Bulletin Pol. Ac.: Tech. 66(4), (2018). (accepted)

10. 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) preprint arXiv:1806.05017.

11. E. Burnaev, A. Cichocki V. Osin, “Fast Multispectral Deep Fusion Networks”, Bulletin Pol. Ac.: Tech. 66(4), (2018). (accepted).


12. A. 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 9.6 (2017): 431-673. (May 2017).

13. 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).

14. J. 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)

15. J. 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.

16. G. 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, 2016. (IF: 5.629)

17. 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)

18. 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.

19. 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)

20. Xie, Z. He, A. Cichocki, X. Fang “Rate of Convergence of the FOCUSS Algorithm”, IEEE Transaction on Neural Networks and Learning Systems (2016).

21. 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)

22. Baumert, M.; Porta, A.; Cichocki, A. “Editorial Biomedical Signal Processing: From a Conceptual Framework to Clinical Applications” Proceedings of the IEEE, (2016)

Awarded IEEE Fellow in 2013.

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

The best paper award in Journal Entropy in 2015
for the paper “Generalized Alpha-Beta Divergences and their Application to Robust Nonnegative Matrix Factorization” Entropy 2011, 13(1), 134–170; doi:10.3390/e13010134

The Best paper award in Journal Entropy for 2014 for the paper couthored by Andrzej Cichocki and Shun-ichi Amari,
“Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities”

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”

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