[2][3] 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) Factorizations for ICA, NMF,  neural networks for optimization problems and signal processing, Tensor  network  for Machine Learning and Big Data, and brain–computer interfaces.

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.

[7] 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.

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.

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).