Andrzej Cichocki (born 1947) is a Polish computer scientist, electrical engineer, biocyberneticist, and academic teacher who holds the titles of Professor of Technical Sciences and Habilitated Doctor. He serves as a professor at the Systems Research Institute of the Polish Academy of Sciences in Warsaw and at Nicolaus Copernicus University in Toruń, and is also affiliated with the Warsaw University of Technology. In addition, he has served as a visiting professor at several institutions, including RIKEN AIP and the RIKEN Brain Science Institute in Japan, as well as the Tokyo University of Agriculture and Technology. He is regarded as one of the leading Polish computer scientists and researchers in the fields of artificial intelligence, biocybernetics, bionics, neural engineering, electronics, electrical engineering, and biomedical signal processing.^[2,3,28]

Cichocki is best known for his learning and signal processing algorithms for blind source separation (BSS), independent component analysis (ICA), non-negative matrix factorization (NMF), tensor decomposition, deep (multi-layer) matrix and tensor factorizations for ICA and NMF, neural networks for optimization problems and signal processing, tensor networks for machine learning and big data, and brain–computer interfaces. He has authored more than 800 peer-reviewed scientific articles as well as several monographs and books, including six in English.^[5,6,4]

He was named to the Web of Science (Clarivate) annual Highly Cited Researchers lists from 2021 to 2023 in the cross-field category, ranking among the top 0.1% most cited scientists in the world whose citations place them in the top 1%.^[1,15,29] In 2025, he was awarded the Dennis Gabor Award, an annual distinction presented by the International Neural Network Society (INNS) recognizing outstanding, long-term, and breakthrough achievements in neural engineering and artificial neural network applications.^[30,31] His honors also include being an IEEE Fellow, a Humboldt Scholar / Prize recipient, a recipient of multiple best paper awards, and a laureate of the Golden Book of Alumni of the Warsaw University of Technology.

Andrzej Cichocki received his M.Sc. Eng. (with honors/distinction), Ph.D., and Doctor of Science (Dr.Sc. / habilitation) degrees in technical sciences in the field of electrical engineering and computer science from the Warsaw University of Technology in Poland. He received the title of full professor in 1995. From 1984 to 1989, he was an Alexander von Humboldt Research Fellow in the FRG and a DFG visiting scholar as well as principal investigator (PI) of DFG grants at the University of Erlangen-Nuremberg, where he collaborated closely with Professor Rolf Unbehauen.^[7]

From 1996 to 2018, he worked at the RIKEN Brain Science Institute (BSI) in Wako-shi, Japan, in the research department headed by Professor Shun'ichi Amari, serving as a research team leader and later as senior head of laboratories / laboratory director. At RIKEN BSI, he established and directed three laboratories: Open Information Systems, Artificial Brains Systems (also designated as the Laboratory for Artificial Neural Networks), and Cichocki's Laboratory for Advanced Brain Signal Processing. He held a distinguished visiting professorship at several universities, including Hangzhou Dianzi University in Hangzhou, China, and Tokyo University of Agriculture and Technology (TUAT) in Tokyo, Japan, with English Wikipedia stating this tenure lasted from 2018 to 2022, while Polish Wikipedia records it as spanning 2018 to 2024.

Andrzej Cichocki has made significant contributions to several fields, including electrical signal and image processing, machine learning, artificial intelligence, artificial neural networks, and brain-computer interface systems. He developed new algorithms for Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Blind Signal Separation (BSS), including the Cichocki–Unbehauen Algorithm for BSS and the Hierarchical Alternating Least Squares (HALS) algorithm for NMF.^[8] He pioneered the development and application of new divergences and divergence measures (including beta, alpha-beta, and other divergences) in machine learning, particularly for multiplicative gradient algorithms, non-negative matrix factorizations, and non-negative tensor decompositions. Furthermore, he pioneered the study and development of multilayer (deep) matrix and tensor factorization models and machine learning algorithms, especially for ICA, NMF, and Sparse Component Analysis (SCA).^[10] He also developed and proposed new recurrent neural network architectures for optimization, solving large-scale systems of algebraic equations, and blind signal separation, specifically multilayer (deep) hierarchical neural networks. In addition, he contributed to the development of natural gradient algorithms for Independent Component Analysis, blind signal separation, and blind deconvolution.^[13,14]

Together with his co-workers, he developed and investigated over a dozen new artificial neural network models and efficient mathematical machine learning algorithms for brain-computer interfaces, human emotion recognition, and the early diagnosis of brain diseases such as Alzheimer's disease and schizophrenia. Following concerns raised by artificial intelligence experts regarding the potential threats and risks of Artificial General Intelligence (AGI) to humanity, Cichocki analyzed and suggested in 2021 the development of novel AGI systems equipped with multiple intelligences, including social-emotional intelligence as well as ethical/moral intelligence combined with self-awareness and responsible decision-making abilities.

His current research interests encompass tensor decomposition and tensor networks in artificial intelligence, learning and machine learning on non-stationary data, and the data fusion of multimodal structured data alongside deep neural network compression. They also include bionics and biocybernetics, time series forecasting and analysis, online portfolio selection (OLPS) and mathematical aspects of investment portfolio optimization in economics, as well as exponentiated/multiplicative exponential gradient and natural gradient learning algorithms for various applications in artificial intelligence. Furthermore, his interests cover applications involving EEG, NIRS, ECoG, EMG, and fMRI in brain-computer interfaces, computational neuroscience, and computer vision, along with Artificial General Intelligence featuring multiple intelligences and the development of safe artificial intelligence.

Andrzej Cichocki has authored and co-authored several English-language books and monographs. With Rolf Unbehauen (spelled Rolf Unbehaen in English Wikipedia), he co-authored MOS Switched-Capacitor and Continuous-Time Integrated Circuits and Systems (subtitled Analysis and Design and published by Springer Science & Business Media), listed as published in 1989 in English Wikipedia and in 2012 in Polish Wikipedia. In 1993, Cichocki and Unbehauen published Neural Networks for Optimization and Signal Processing (also titled Neural Networks for Optimization and Signal Processing Problems) with John Wiley & Sons, Inc. In 2002, he published Adaptive Blind Signal and Image Processing (titled Adaptive Signal and Image Processing: Learning Algorithms and Applications in Polish Wikipedia; ISBN 9780470845899) alongside Shun-Ichi Amari with John Wiley & Sons. In 2009, Cichocki, Rafal Zdunek, Anh Huy Phan, and Shun-Ichi Amari published Nonnegative Matrix and Tensor Factorizations (subtitled Applications to Exploratory Multi-way Data Analysis and Blind Source Separation) with John Wiley & Sons. In 2016, Cichocki, Qibin Zhao, Anh-Huy Phan, Ivan Oseledets, Namgil Lee, and Danilo Mandic (Danilo P. Mandic) published Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 (subtitled Low-Rank Tensor Decompositions in English Wikipedia) in Foundations and Trends in Machine Learning (volume 9, issues 4–5, pages 249–429). In 2017, Cichocki, Masashi Sugiyama, Qibin Zhao, Anh-Huy Phan, Ivan Oseledets, Namgil Lee, and Danilo P. Mandic published Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives in Foundations and Trends in Machine Learning (volume 9, issue 6, pages 431–673; doi: 10.1561/2200000067).

Andrzej Cichocki was the winner of the Alexander von Humboldt Award in Germany in 1984–1985. In 1995, he received the title of Professor in Poland from the President of the country. In 2013, he was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for contributions to applications of blind signal processing and artificial neural networks, and was honored with an entry in the Golden Book of Alumni of Warsaw University of Technology for his professional achievements. He was awarded an Honoris Causa Doctorate by Nicolaus Copernicus University in Toruń, Poland, on February 27, 2022 (listed under 2018). Cichocki was also included in Clarivate Web of Science's Highly Cited Researchers list from 2021 to 2023.

He received the APNNA Best Paper Award in 2010 for the paper "A tongue-machine interface: Detection of tongue positions by glossokinetic potentials," coauthored with Yunjun Nam, Qibin Zhao, and Seungjin Choi and presented at the International Conference on Neural Information Processing (ICONIP-2010) in Sydney, Australia. He won the 2014 Best Paper Award in the journal Entropy for the paper "Families of Alpha- Beta- and Gamma- Divergences: Flexible and robust measures of similarities," coauthored with Shun'ichi Amari. In 2015, he received the Best Paper Award in the journal Entropy for "Generalized Alpha-Beta divergences and their application to robust non-negative matrix factorization" (Entropy 2011, 13(1), 134–170), coauthored with S. Cruces and S. Amari. He was awarded the 2016 Excellent ICONIP Paper Award for the paper "Nonnegative tensor train decompositions for multi-domain feature extraction and clustering," coauthored with Namgil Lee, Anh-Huy Phan, and Fengyu Cong. In 2018, he received the Best Paper Award in IEEE Signal Processing Magazine for the paper "Tensor decompositions for signal processing applications: From two-way to multiway component analysis," coauthored with D. Mandic, L. De Lathauwer, A. H. Phan, Q. Zhao, C. Caiafa, and G. Zhao.

External resources include Andrzej Cichocki's homepage at the Systems Research Institute of the Polish Academy of Science, his identifier wpZDx1cAAAAJ, and an interview with him. Further materials concerning the Cichocki Laboratory feature a CNN interview regarding a wheelchair controlled by EEG signals, an NTT presentation in Japanese on new developments, and documentation of research at the laboratory in Riken from 2010.