Plamen Parvanov Angelov

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 of Excellence



Plamen Parvanov Angelov
Lancaster University   UK

Professor Dr. Angelov ( holds a Personal Chair in Intelligent Systems and Leads the Data Science Group at Lancaster University, UK. He is a Senior Member of IEEE and of the International Neural Networks Society (INNS), Fellow of HEA, founding Chair of the Technical Committee (TC) on Evolving Intelligent Systems within the Systems, Man and Cybernetics Society, IEEE and a member of the TCs on Neural Networks and on Fuzzy Systems within the Computational Intelligence Society, IEEE. He has authored or co-authored over 200 peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 4 patents and a dozen books. He has an active research portfolio in the area of data science, computational intelligence and autonomous machine learning and internationally recognised results into online and evolving learning and algorithms for knowledge extraction in the form of human-intelligible fuzzy rule-based systems. Prof. Angelov leads numerous projects funded by UK research councils, EU, industry, UK Ministry of Defence. His research was recognised by ‘The Engineer Innovation and Technology 2008 Special Award’ and ‘For outstanding Services’ (2013) by IEEE and INNS. He is also the founding Editor-in-Chief of Springer’s journal on Evolving Systems and Associate Editor of the leading international scientific journals in this area, including IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems and several others. He was General, Program or Technical Chair of prime IEEE conferences (IJCNN-2013, Dallas; SSCI2014, Orlando,WCCI2014, Beijing; IS’14, Warsaw;  IJCNN2015, Kilkarney; IJCNN/ WCCI2016, Vancouver) and founding General Chair of a series of annual IEEE conferences on Evolving and Adaptive Intelligent Systems, acts as Visiting Professor (in Brazil, Germany, Spain, France) regularly gives invited and plenary talks at leading companies and universities.


Project: Prof. Angelov will hold his Chair of Excellence in Carlos III University starting end of March till September 2015. The topic of the academic activities is Autonomous Learning Systems: from Data Streams to Knowledge in Real Time.  The reality of 21st century poses new challenges which come from the Big data paradigm, explosion of usage of social networks and multimedia, an exponentially growing data streams and cheap computational processing and sensor devices. The bottleneck in the old human desire to extract seamlessly and in real time useful knowledge is now not the hardware or access to data, but the algorithms which are still largely based on decades if not centuries old paradigm of availability of all data (offline, cross validations), their stationarity (changes like the financial crash of 2008 proved to be enigmatic) and limited size which can be stored and communicated/transmitted as we wish (bandwidth limitations). The well known Moore’s law is now more applicable not for the hardware but for the data itself. The phrase ‘digital obesity’ was even coined in 2006 by the Japanese corporation Toshiba.
Addressing these challenges the study will focus on development of new approaches for dynamically evolving and autonomously learning systems (ALS). A number of requirements can be defined to such systems: i) the mathematical model structure should not be fixed, but dynamically evolving; ii) complex systems has to be decomposed into smaller (possibly overlapping) sub-systems/models (the old Latin sentence Divide et impera); iii) adapting to new environment and internal changes in terms not only of parameters of the system but also of its structure; iv) reducing the role of the human to start/stop and possibly monitor, but not to provide system structure, for example; v) use computationally efficient recursive calculations and, thus, avoiding iterative solutions and operating on-line in real-time, sample by sample; vi) avoiding use of problem and user-specific thresholds and parameters, in general.
In this half a year (one semester) programme the proposal is to strengthen the CAOS and other related groups. The focus will be on development of new and improved methods for evolving and ALS for real-time knowledge extraction and applying them to i) evolving human behaviour modelling, ii) car driver assistance and robotic/autonomous systems, iii) self-calibrating sensors for chemical, bio- and petro-chemical industry, etc.  

Stay period: MAR 15 - SEP 15