We focus on computational tools as well as their applications to analyze and understand interactions between parts of a complex system and the overall complex system. From such analysis, we aid the decisions of choosing suitable parameters/models, optimizing and guarantee system performance. To this end, we study and develop advanced mathematical tools as well as adaptive algorithms to design and model real-world systems efficiently in bio-medicine, electronics and telecommunication.

Our current research topics include adaptive matrix and tensor analysis, system identification, statistical performance analysis of communication systems and artificial intelligence for communication systems.

Core members

Adaptive matrix and tensor analysis

Large volumes of data are being generated at any given time, especially from transactional databases, multimedia content, social media, and applications of sensor networks. When the size of datasets is beyond the ability of typical database software tools to capture, store, manage, and analyze, we face the phenomenon of big data for which new and smarter data analytic tools are required. Big data provides opportunities for new form of data analytics, resulting in substantial productivity. We explore fast (adaptive) matrix and tensor decompositions as computational tools to process and analyze multidimensional massive-data (with a focus on streaming data). We consider some real-world problems such as Radio Frequency Interference (RFI) Mitigation in radio astronomy or monitoring long and multi-channel EEG data.

Informed system identification

System identification (SI) is necessary in many applications such as control, telecommunication, biomedical signal processing, to name a few, to understand and control the behavior of the considered system. In particular, in the inverse problems it is required to identify the relation between the output and input signals of the system in order to restore or extract some information on the latter. In many situations, one has to handle the identification problem using only the system output signal in addition to structural or statistical information about the system and its inputs. This is referred to as the problem of blind SI. We are interested in “informed” SI, where we have more information to handle the limitation of blind SI. Various types of information are to be considered, such as side information or information obtained from a learning process.

Statistical performance analysis of telecommunication systems

We use tools from statistics, probability theory and stochastics processes to achieve performance bounds for communication systems of interest.  Then, parameters of system configuration can be optimized to reach optimal performance.

Other information

Contact: Dr. Nguyen Viet Dung, Group leader, AVITECH