Personal Websites

Andrzej Stanislaw Cichocki

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

Dr. Cichocki served many years 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 currently holds visiting Senior Researcher in RIKEN AIP and  a position of Professor in the Systems Research Institute.

Professor Cichocki contributed to blind signal and image processing, deep (multilayer) matrix factorizations, learning algorithms for Independent Component Analyis (ICA), Nonnegative Matrix and Tensor Factorizations (NMF/NTF), Brain Computer Interface (BCI), deep and recurrent  neural networks, and tensor networks for applications in AI and machine learning. He is a holder of numerous awards and author of  highly cited and well-known publications and 6 monographs in English (three of them translated into Chinese). According  to Google Scholar he has h-index above 116,  with more than 62,000 citations.
Dr. Cichocki was a winner of numerous awards for his scientific achievements, best conference presentations and best journal publications, e.g.,  the paper about tensor decompositions published in the IEEE Signal Processing Magazine  (2019) and  in Entropy about Alpha-Beta divergences (2014 and 2015).

Dr Andrzej Cichocki has been selected for his exceptional influence and performance on the list of
Highly Cited Researchers in 2023, 2022 and 2021 by Web of Science (Clarivate). He scientifically collaborated with scientists worldwide, in more than 50 countries, especially in Poland, China, Japan, Vietnam, Spain, France, Germany,  Russia , Ukraine, UK and USA.

Under the guidance of Professor Cichocki the new Laboratory “Tensor Networks and Deep Learning for Applications
in Data Mining”  was established at SKOLTECH in 2017-2021. The mission of the Laboratory was to perform cutting-edge opened  and transparent research in the design and analysis of deep neural networks, tensor decomposition, tensor networks and multi-way component analysis for biomedical and health care applications and for improving quality of human life.

He is Fellow of IEEE from 2013.  In 2017 he received the title of doctor honoris causa of the University of  the Nicolaus Copernicus University (UMK) Torun, Poland.

His research was focused 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 Tensor Networks

7. Humanoid Robotics and Human Robot Interactions for Rehabilitation

Selected List of international peer-reviewed publications in 2016-2021

Peer-reviewed journal Publications (Web of Science)

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.)

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).

Cichocki, A., & Kuleshov, A. P. (2021). Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles.
Computational Intelligence and Neuroscience, 2021. and in Russian “Искусственный Интеллект Будущего:
Множественный Интеллект и Методы его Обучения”

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 Systems, (early access), 2020 (IF=11.68).

Yokota, T., Hontani, H., Zhao, Q., & Cichocki, A. (2020). “Manifold Modeling in Embedded Space:
An Interpretable Alternative to Deep Image Prior”. IEEE Transactions on Neural Networks and Learning Systems

Chen, Z., Jin, J., Daly, I., Zuo, C., Wang, X., & Cichocki, A. (2020). “Effects of Visual Attention on Tactile P300 BCI”. Computational Intelligence and Neuroscience2020.

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).

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).

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.

 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. 

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.

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.

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. 

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.

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.

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.

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).  

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).

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)

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.

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.

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. 

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. 

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).  

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.

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).

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”. Neurocomputing!

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.

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.

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.

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.

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.

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.

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.

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. 

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.

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).!

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.)

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.

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. Impact Factor 4.79.

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.

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 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.

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.

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.

Tichavský, P., Phan, A. H., & Cichocki, A. (2017). “Non-orthogonal tensor diagonalization”. Signal Processing, 138, 313-320. Impact Factor: IF=4.7

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. Impact factor is 3.211.

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.

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)

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)

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.

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)

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

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)

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

Research Monographs (Books in English two of them translated into Chinese)

  • 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).
  • 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).
  • 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).

Selected talks / presentations published  in proceedings of  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.

Prof. Cichocki has been selected for inclusion on the annual Highly Cited Researchers™ 2023, 2022

and 2021  list from Clarivate Analytics (Web of Science).

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

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”

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”

Salman Ahmad Asl
Senior Research Scientist
Gyamfua Asante-Mensah
PhD student

ФИО:  Чихоцкий Анджей Станислав

Занимаемая должность (должности): ?

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