Biography:Andrzej Cichocki

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Short description: Polish computer scientist
Andrzej Cichocki 
Born1947
Poland
NationalityPoland
Alma materWarsaw University of Technology
AwardsHumboldt Prize
Fellow IEEE
Recipient of several best papers awards
Scientific career
FieldsMachine Learning
Tensor Networks
Signal and Image Processing
Brain-computer interface
Independent Component Analysis
Non-negative matrix factorization
InstitutionsSystems Research Institute Polish Academy of Sciences
Warsaw University of Technology
Nicolaus Copernicus University
RIKEN Brain Science Institute

Andrzej Cichocki (born 1947) is a Polish computer scientist, electrical engineer and a professor at the Systems Research Institute of Polish Academy of Science, Warsaw, Poland and a visiting professor in several universities and research institutes, especially Riken AIP, Japan. He is most noted for his learning algorithms for   Signal separation (BSS), Independent Component Analysis (ICA), Non-negative matrix factorization (NMF), tensor decomposition,     Deep (Multilayer) Matrix Factorizations for ICA, NMF, PCA,  neural networks for optimization and signal processing, Tensor  network  for Machine Learning and Big Data, and brain–computer interfaces. He is the author of several monographs/books [1] and more than 500 scientific peer-reviewed articles.[2]

Education and Career

Andrzej Cichocki in 2013 in Riken Brain Science Institute

Andrzej Cichocki received his M.Sc. (with honors), PhD and doctor of science (Dr.Sc.- Habilitation) degrees all in electrical engineering and computer science from the Warsaw University of Technology, Poland.

He received the title of full Professor in 1995.

From 1984 to 1989 he was a Alexander von Humboldt Research Fellow and DFG visiting scholar at the University of Erlangen Nurnberg, Germany and he worked closely with Professor Rolf Unbehauen.

From 1996 till 2018 he worked in RIKEN Brain Science Institute, Wako-shi, Japan at Shun'ichi Amari's Research Department, as a team leader and later as senior head of laboratories. He established and ran in RIKEN BSI three laboratories: Open Information Systems, Artificial Brains Systems and Cichocki's Laboratory for Advanced Brain Signal Processing.

In 2018-2022 he holds a distinguished visiting professorship at several universities including Hangzhou Dianzi University in Hangzhou, China and Tokyo University of Agriculture and Technology (TUAT), Tokyo, Japan.

Research

He is one of the leading computer scientists affiliated with Poland.[3]

Andrzej Cichocki has contributed extensively to several major interests of signal/image processing, machine learning and AI, including Independent Component Analysis (ICA), Non-negative matrix factorization (NMF) and artificial neural networks. He developed an efficient Hierarchical Alternating Least Squares (HALS) algorithm.[4][5]

He pioneered developing and applying new beta and alpha-beta and other divergences in machine learning, especially for non-negative matrix factorizations and nonnegative tensor decompositions. Moreover, he pioneered in development of multilayer (deep) matrix and tensor factorization models and learning algorithms, especially for ICA, NMF and Sparse Component Analysis (SCA).[6][7][8] He developed and proposed new recurrent neural network architectures for optimization, solving large scale systems of algebraic equations and blind signal separation, especially multilayer (deep) hierarchical neural networks. He contributed to development of natural gradient algorithms for Independent Component Analysis (ICA) and blind deconvolution.[9][10]

He proposed together with his co-workers several efficient AI models and machine learning algorithms for brain computer interface, human emotions recognition and early diagnosis of some brain diseases, like Alzheimer and Schizophrenia.

Following concerns raised by some  AI experts about the potential risks that  AGI  may pose  on humanity, Cichocki  suggested   in 2021 development of novel   AGI  systems  with implemented multiple intelligences, including  not only ethical/moral  intelligence but also social-emotional intelligence with self-awareness and responsible decision making abilities.

His current research interests include:

  • Tensor decomposition and tensor networks
  • Learning of non-stationarity data
  • Data fusion of multi-modal structured data, and deep neural networks compression
  • Applications: EEG, NIRS, ECoG, EMG, Brain Computer Interfaces, computational neuroscience, computer vision.
  • Time series forecasting and analysis
  • Online portfolio selection (OLPS)
  • Exponentiated gradient and natural gradient learning algorithms for various applications
  • Artificial General Intelligence (AGI) with multiple intelligences

Books

  • Cichocki, Andrzej, & Unbehauen, Rolf (1993). Neural Networks for Optimization and Signal Processing. John Wiley & Sons, Inc.. ISBN:978-0-471-93010-5,doi:10.1002/acs.4480080309
  • Cichocki, A., & Amari, S. I. (2002). Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons. ISBN:9780470845899, doi:10.1002/0470845899
  • Cichocki, A., Zdunek, R., Phan, A. H., & Amari, S. I. (2009). Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. John Wiley & Sons. ISBN:9780470747278, doi:10.1002/9780470747278
  • Cichocki, A., Lee, N., Oseledets, I., Phan, A. H., Zhao, Q., & Mandic, D. P. (2016). Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 1 Low-Rank Tensor Decompositions. Foundations and Trends® in Machine Learning, 9(4-5), 249-429.  doi:10.1561/2200000059  
  • Cichocki, A., Phan, A. H., Zhao, Q., Lee, N., Oseledets, I., Sugiyama, M., & Mandic, D. P. (2017). Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives. Foundations and Trends® in Machine Learning, 9(6), 431-673. doi:10.1561/2200000067
  • Rolf Unbehauen & Andrzej Cichocki, (2012). MOS Switched-Capacitor and Continuous-time Integrated Circuits and Systems: Analysis and Design. Springer Science & Business Media. ISBN:978-3-642-83677-0,doi:10.1007/978-3-642-83677-0

Awards and honors

  • 2021, 2022 and 2023 Andrzej Cichocki is listed among Clarivate's Web of Science Highly Cited Researchers[11][12]
  • 2018 The best paper award in  2018 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, Zhao  [13]
  • 2018 H.C. (Honoris Causa) Doctorate, awarded by  Nicolaus Copernicus University, Torun, Poland, February 27, 2022 [14]
  • 2016 Excellent ICONIP Paper Award for the paper authored by Namgil Lee, Anh-Huy Phan, Fengyu Cong, Andrzej Cichocki. “Nonnegative tensor train decompositions for multi-domain feature extraction and clustering”
  • 2015 The best paper award in Journal Entropy  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 [15]
  • 2014 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” [16]
  • 2013 Andrzej Cichocki was named Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013[17] for contributions to applications of blind signal processing and artificial neural networks.
  • 2010  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.
  • 1995  Received a title of Professor in Poland from the President of the country
  • 1984-1985  Winner of Alexander von Humboldt Award in Germany.

References

  1. ↑ "Books Authored by Andrzej Cichocki". https://www.amazon.com/Books-Andrzej-Cichocki/s?rh=n%3A283155%2Cp_27%3AAndrzej+Cichocki. 
  2. ↑ "Highly Cited Researchers 2021, 2022 recipients" (in en). Clarivate. https://www.webofscience.com/wos/author/record/1969193. Retrieved 2023-08-08. 
  3. ↑ "Best Scientists - Computer Science". Guide 2 Research. https://research.com/u/andrzej-cichocki. Retrieved 2023-08-08. 
  4. ↑ Cichocki, Andrzej; Zdunek, Rafal; Amari, Shun'ichi (2007). "Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization". Independent Component Analysis and Signal Separation. Lecture Notes in Computer Science. 4666. pp. 169–176. doi:10.1007/978-3-540-74494-8_22. ISBN 978-3-540-74493-1. https://www.researchgate.net/publication/220848206. 
  5. ↑ Cichocki, Andrzej; Phan, Anh-Huy (2009). "Fast local algorithms for large scale nonnegative matrix and tensor factorizations". IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 92 (3): 708–721. doi:10.1587/transfun.E92.A.708. Bibcode: 2009IEITF..92..708C. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4229f467b059188fc7a1234016a3c80557fa7df0. 
  6. ↑ Cichocki, Andrzej; Kasprzak, Wlodzimierz; Amari, Shun-ichi (1995). "Multi-layer neural networks with a local adaptive learning rule for blind separation of source signals". Proceedings of the 1995 International Symposium on Nonlinear Theory and Its Applications (NOLTA'95): 61–65. https://www.ia.pw.edu.pl/~wkasprza/PAP/CKA_nolta95.pdf. 
  7. ↑ Cichocki, Andrzej; Kasprzak, Wlodzimierz (1997). "Local adaptive learning algorithms for blind separation of natural images". Neural Network World: 515–523. https://www.ia.pw.edu.pl/~wkasprza/PAP/NNW_6_1996_515.pdf. 
  8. ↑ Cichocki, Andrzej; Zdunek, Rafal (2007). "Multilayer nonnegative matrix factorization using projected gradient approaches". International Journal of Neural Systems 17 (6): 431–446. doi:10.1142/S0129065707001275. PMID 18186593. https://www.academia.edu/19190276. 
  9. ↑ Amari, Shun'ichi; Cichocki, Andrzej; Young, Howard (1995). "A new learning algorithm for blind signal separation". Proc. Advances in Neural Information Processing Systems 8 Advances in Neural Information Processing Systems 8: 757–763. https://proceedings.neurips.cc/paper/1995/file/e19347e1c3ca0c0b97de5fb3b690855a-Paper.pdf. 
  10. ↑ Cichocki, Andrzej; Unbehauen, Rolf (1996). "Robust neural networks with on-line learning for blind identification and blind separation of sources". IEEE Transaction on Circuits and Systems Systems 43 (11): 894–906. doi:10.1109/81.542280. https://www.researchgate.net/publication/3322862. 
  11. ↑ "Highly Cited Researchers 2023, 2022, 2021 recipients" (in en). Clarivate. https://www.webofscience.com/wos/author/record/AAI-4209-2020/. Retrieved 2023-11-11. 
  12. ↑ "Highly Cited Researchers according to Web of Science". https://recognition.webofscience.com/awards/highly-cited/2021/. 
  13. ↑ " Best paper award for 2018 in IEEE Signal Processing Magazine". IEEE SPS =2019-01-20.  20 December 2018. https://signalprocessingsociety.org/newsletter/2019/01/2018-ieee-signal-processing-society-awardees. 
  14. ↑ [ https://naukawpolsce.pl/aktualnosci/news%2C28341%2Ctorun-prof-andrzej-cichocki-doktorem-honoris-causa-umk.html/ "Andrzej Cichocki received Doctorate Honoris Causa"]. UMK, Nauka w Polsce =2018-02-27.  https://naukawpolsce.pl/aktualnosci/news%2C28341%2Ctorun-prof-andrzej-cichocki-doktorem-honoris-causa-umk.html/. 
  15. ↑  Knuth,  Kevin H. ( February 2015). " Best paper award for 2015 in the Journal Entropy".  Entropy (mdpi.com =2015-12-20)  17 ( 2):  882–884. doi:10.3390/e17020882. 
  16. ↑ Knuth, Kevin H. (February 2014). "Best paper award for 2014 in the Journal Entropy". Entropy (mdpi.com =2014-12-20) 16 (2): 726–728. doi:10.3390/e16020726. 
  17. ↑ "2013 elevated fellow". https://ieeexplore.ieee.org/author/37266683500. 

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