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1973 …2022

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Personal profile

Research interests

Professor Andrzej Cichocki graduated from the Warsaw University of Technology, Poland, where he obtained his PhD and Doctor of Science degree (Habilitation) in Electrical Engineering and Computer Science  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 analytic technologies. Andrzej 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 universities in Poland, 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  highly cited and well-known publications and 6 monographs in English. According  to Google Scholar he has h-index 93 with more than 42,000 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  paper about tensors published in the  IEEE Signal Processing Magazine  (2019) and  in Entropy about alpha-beta divergences (2014 and 2015). He is one of the most cited Polish scientist in area of AI, Computer Science and Engineering.

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 opened research in the design and analysis of deep neural networks, tensor decomposition, tensor networks and multiway component analysis for  biomedical applications and improving quality of life. He is Fellow of IEEE from 2013.

His research focus on

1. AI, especially Deep Neural Networks, Unsupervised and Supervised Algorithms

2. Tensor Networks and Tensor Decomposition for  Machine Learning

3. EEG Brain Computer Interfaces, Human Computer Interactions and Human Cooperation

4. Novel Signal Processing and  Machine Learning Algorithms

5. Portfolio Optimization and Time Series Analysis

6. Time Series Forecasting Using AI

7. Humanoid Robotics and Human Robot Interactions

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 11 - Sustainable Cities and Communities


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