Biography: Marios M. Polycarpou is a Professor of Electrical and Computer Engineering and the Director of the KIOS Research Center for Intelligent Systems and Networks at the University of Cyprus. He received undergraduate degrees in Computer Science and in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and cooperative control systems, computational intelligence, fault diagnosis and distributed agents. Dr. Polycarpou has published more than 300 articles in refereed journals, edited books and refereed conference proceedings, and co-authored 7 books. He is also the holder of 6 patents.
Prof. Polycarpou is a Fellow of IEEE and IFAC, and past IEEE Distinguished Lecturer of Computational Intelligence. He is the recipient of the 2016 IEEE Neural Networks Pioneer Award and the 2014 Best Paper Award for the journal Building and Environment (Elsevier). He has served as the President of the IEEE Computational Intelligence Society (2012-2013), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010). He is currently the Vice President of the European Control Association (EUCA). Prof. Polycarpou has participated in more than 60 research projects/grants, funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant.
Title of Speech: Smart Buildings: a Platform for Computational Intelligence
Abstract: Modern buildings are complex systems of structures and technology aimed at providing a safe and comfortable environment for the occupants. Recent advances in information and communication technologies have generated significant interest in developing smart buildings, which provide much greater capabilities in terms of energy efficiency, safety, security, interactivity, as well as in terms of mitigating environmental impact. New components for smart buildings, such as sensors, actuators, controllers, embedded systems and wireless communications, are becoming readily available. Moreover, the Internet-of-Things (IoT) technology is already having a significant impact on developments related to smart buildings. The objective of this presentation is to provide an overview of current advances in smart buildings and key challenges in the years ahead. Specific emphasis is given to issues related to the “intelligence” inside the smart buildings, and how they can serve as a platform for computational intelligence concepts and methodologies. Various estimation, learning and feedback control algorithms will be presented and illustrated, and directions for future research will be discussed.
Biography: Robert Kozma is Professor of Mathematics and Funding Director of the Center for Large-Scale Integrated Optimization and Networks (CLION), University of Memphis, TN; he is also on the faculty of Computer Science, University of Massachusetts Amherst, USA. Dr. Kozma has Ph.D. in Applied Physics (The Netherlands); MSc. in Mathematics (Hungary), and another M.Sc. in Physical Engineering (Russia). He is Fellow of IEEE, Fellow of Interational Neural Network Society (INNS), and President of INNS (2017- 2018). He has served on the faculty of Division of Neuroscience, UC Berkeley; Tohoku University, Japan; Otago University, New Zealand. He has held visiting positions at Sarnoff Co., Princeton, NJ; JPL Robotics Lab, Pasadena; AFRL Sensors Directorate, HAFB & WPAFB, and Lawrence Berkeley Lab. His research interest includes spatio-temporal neurodynamics, large-scale networks, intelligent signal processing, and brain-computer interfaces. His research has been founded by agencies including DARPA, NSF, NASA, JPL, AFOSR, AFRL, and NRC. He is author of over 300 referred papers, 7 book volumes. He is the receipient of a number of awards, including the INNS Gabor Award.
Title of Speech: New AI and Advances in Brain Science: A Phase Transition Ahead
Abstract: Recent experiments with high-resolution brain imaging techniques provide an amazing view on the complex spatio-temporal dynamics of cortical processes. Significant resources are concentrated in the US and worldwide in mega-projects focusing on multi-level and multiresolution neural and brain models. These efforts hold the promise of developing novel AI tools that go beyond formal manipulations of symbolic rule based systems, thus avoiding many problems inherently present in traditional AI approaches, such as rigidity, inefficiency, and inability of robust operation in dynamically changing environment.
New AI approaches involving deep learning using large-scale databases demonstrate great promise in many applications including image processing, voice recognition, and game playing. We review directions of recent progress and future potentials based on these new biological and cognitive insights. According to modern theories of cognition and consciousness, brains can be perceived as open thermodynamic systems converting sensory data into meaningful knowledge during a repetitive process of phase transitions. Cortical phase transitions are viewed as neural correlates of higher cognition, which can be implemented in hardware domains to develop new principles of intelligent computing. These new approaches will lead to breakthroughs in understanding brain operations and in building machines with superior AI.