Trinity College Dublin, Ireland
Principles and Practice of Self-Organization in 6G Networks
Nicola Marchetti is currently Assistant Professor in Wireless Communications at Trinity College Dublin, Ireland. He performs his research under the Irish Research Centre for Future Networks and Communications (CONNECT), where he leads the Wireless Engineering and Complexity Science (WhyCOM) lab. He received the PhD in Wireless Communications from Aalborg University, Denmark in 2007, and the M.Sc. in Electronic Engineering from University of Ferrara, Italy in 2003. He also holds an M.Sc. in Mathematics which he received from Aalborg University in 2010. His collaborations include research projects in cooperation with Nokia Bell Labs and US Air Force Office of Scientific Research, among others. His research interests include Radio Resource Management, Self-Organising Networks, Complex Systems Science and Signal Processing for communication networks. He has authored in excess of 120 journals and conference papers, 2 books and 8 book chapters, holds 3 patents, and received 4 best paper awards. He is a senior member of IEEE and serves as an associate editor for the IEEE Internet of Things Journal since 2018 and the EURASIP Journal on Wireless Communications and Networking since 2017.
As 5G networks are already being standardised and deployed, the focus of the research community has now shifted toward the next generation of communication networks, the so called 6G. Some main trends being consolidated are in terms of the integration of cellular networks with the Internet of Things, Industry 4.0, and Artificial Intelligence. Many more exciting novelties are expected in the next network generation, including highly mobile communication infrastructure, and novel propagation conditions spanning all the way from the nano-scale to large scale systems such as satellite and space ones. In this context, severe challenges lie ahead in terms of communication systems and networks’ scalability and the associated very low latency and very high reliability requirements, requiring new mathematical and engineering approaches that heavily rely on distributed and self-organizing principles. Several ideas and tools drawing from complex systems science will be discussed, in terms of their relevance for the analysis, design and operation of 6G self-organizing networks.
University of Pavia, Italy
Modern Sliding Mode Control with Applications
Short Bio: Antonella Ferrara was born in Genova, Italy, in 1963. As a student at the Faculty of Engineering of the University of Genova, she got the “IEEE North Italy Section Electrical Engineering Student Award” in 1986. She received the Laurea Degree in Electronic Engineering in 1987 and the Ph.D. in Computer Science and Electronics in 1992 from the University of Genova. Since January 2005 she is Full Professor of Automatic Control, first in the Department of Computer Engineering and Systems Science of the University of Pavia (2005-2011), and then in the newborn Department of Electrical, Computer and Biomedical Engineering (ECBE) of the same university.
She teaches “Process Control and Robotics” and “Advanced Automation and Control” (both in English) in the Master Degree Programs in Computer Engineering and Industrial Automation Engineering. She also teaches “Control of Vehicle Dynamics” (in English) in the Post-Bachelor Program on Design and Development of Vehicle Dynamics. She was Visiting Professor at Graz University of Technology, and Invited Lecturer at Harvard University, University of Minnesota, University of California at Los Angeles (UCLA), University of Stuttgart, Technical University of Delft, INRIA Grenoble, King Abdullah University of Science and Technology (KAUST), Jeddah, Hanyang University, Seoul, and Universidade Federal do Rio de Janeiro, Brazil.
Her research activities are mainly in the area of advanced automatic control of complex systems, with application to mechanical systems, automotive control, robotics, power systems control, process control, and vehicular traffic control.
She was and still is a coordinator and member of several research units in national and European research projects, and scientific leader of several research projects funded or co-funded by companies. She has authored or co-authored more than 370 scientific papers, with 115 international journal papers, 2 published scientific books and a third book to appear soon. Moreover, she contributed, with invited chapters, to 23 edited books.
Sliding Mode Control is a nonlinear control methodology based on the use of a discontinuous control input which forces the controlled system to switch from one structure to another, evolving as a variable structure system. This structure variation makes the system state reach in a finite time a pre-specified subspace of the system state space where the desired dynamical properties are assigned to the controlled system. In the past years, an extensive literature has been devoted to the developments of Sliding Mode Control theory. This kind of methodology offers a number of benefits, the major of which is its robustness versus a significant class of uncertainties and disturbances during the sliding mode. Yet, it presents a crucial drawback, the so-called chattering, which may disrupt or damage the actuators and induce unacceptable vibrations throughout the controlled system. This limits the practical applicability of the methodology, especially in case of mechanical or electromechanical plants. This drawback has been faced by the theoretical developments of the last two decades. They will be reviewed in this talk, illustrating how the “modern” results can be profitably used to solve practical control problems in complex systems as the automotive ones.
University of Pittsburgh, USA
The role of data science in understanding human swallowing and gait functions
Biography: Dr. Ervin Sejdić received B.E.Sc. and Ph.D. degrees in electrical engineering from the University of Western Ontario, London, Ontario, Canada in 2002 and 2008, respectively. From 2008 to 2010, he was a postdoctoral fellow at the University of Toronto with a cross-appointment at Bloorview Kids Rehab, Canada’s largest children’s rehabilitation teaching hospital. From 2010 until 2011, he was a research fellow at Harvard Medical School with a cross-appointment at Beth Israel Deaconess Medical Center. From his earliest exposure to research, he has been eager to contribute to the advancement of scientific knowledge through carefully executed experiments and ground-breaking published work. This has resulted in co-authoring over 130 publications. In February 2016, President Obama named Dr. Sejdić as a recipient of the Presidential Early Career Award for Scientists and Engineers. In 2017, Dr. Sejdić was awarded the National Science Foundation CAREER Award. In 2018, he was awarded the Chancellor’s Distinguished Research Award at the University of Pittsburgh. Dr. Sejdić’s passion for discovery and innovation drives his constant endeavors to connect advances in engineering to society’s most challenging problems. Hence, his research interests include biomedical signal processing, gait analysis, swallowing difficulties, advanced information systems in medicine, rehabilitation engineering, assistive technologies and anticipatory medical devices.
A human body comprises of several physiological systems that carry out specific functions necessary for daily living. Traumatic injuries, diseases and aging negatively impact human functions, which can cause a decreased quality of life and many other socio-economical and medical issues. Accurate models of human functions are needed to propose interventions and treatments that can restore deteriorated human functions. Therefore, our research aims to develop novel data analytics and instrumentation approaches that can accurately assess swallowing and gait functional losses due to aging and neurological disorders. More than 30 million of Americans will have some sort of swallowing and/or gait issues in the near future. The current-state-of-art approaches do not provide reliable answers how to properly assess and manage these functional losses in daily lives. This is a critical issue, as health costs associated with swallowing and gait functional losses outpace other aging-related health issues. In this talk, I will present our recent contributions dealing with both engineering and clinical aspects of our work aimed at addressing current knowledge gaps. Lastly, I will also present our future research goals and our strategy to achieve these goals.
University of Notre Dame, USA
Improving human health by analyzing biological or social networks
Biography: Dr. Tijana Milenkovic is an Associate Professor of Computer Science and Engineering at the University of Notre Dame. She has been a Notre Dame faculty since 2010, after getting her Ph.D. in Computer Science from the University of California Irvine in the same year. Milenkovic leads the Complex Networks (CoNe) Lab, which solves challenging problems in the fields of network science, data mining, algorithms, computational biology, social networks, and scientific wellness. Her work focuses on developing computational methods for modeling complex real-world systems as networks and for efficiently mining the networks to learn how the systems function. At the same time, through interdisciplinary collaborations with researchers from biology, chemistry, social sciences, psychology, etc., Milenkovic aims to demonstrate practical usefulness of her methods in the different domains. She won prestigious 2015 National Science Foundation (NSF) CAREER and 2016 Air Force Office of Scientific Research (AFOSR) YIP awards. Also, she has been the lead Principal Investigator on four other awards by NSF or National Institutes of Health (NIH). She has published 41 journal papers (e.g., in Science, PNAS, Nature’s Scientific Reports, or Bioinformatics) and 13 conference papers (including three papers in the top tier ISMB/ECCB computational biology conference). She has been an Associate Editor of IEEE/ACM TCBB since 2014 and of Nature’s Scientific Reports since 2018. She has acted as an organizing committee member, publication co-chair, workshop/tutorial organizer, and program committee member at a number of prestigious conferences, and as a reviewer for many respectable journals. Milenkovic is committed to integrating research and education, as well as increasing participation of women in computer science. For example, in addition to supervising 10 Ph.D. and 3 M.Sc. students, she has also supervised 21 undergraduate researchers, 7 of whom continued onto Ph.D. programs at e.g., Stanford, MIT, or Carnegie Mellon University. Also, she has organized research workshops at a career conference for middle school girls yearly since 2012, to motivate them to pursue future careers in STEM, including computer science.
Both graphite and diamond are made of the same elements, namely carbon atoms. So why is graphite soft and dark, while diamond is hard and clear? It is the connections between the carbon atoms that give rise to these different properties of graphite and diamond. Hence, connections (or interactions) between the elements matter. Such interactions are captured by the notion of networks (graphs), which are an elegant choice for modeling and analyzing many complex, highly interconnected real-world systems, such as the Internet, World Wide Web, Facebook, ecological food webs, or molecular systems such as the brain or cells. Owing to the exponential growth of available real-world network data, as well as their noisy, heterogeneous, and dynamic nature, the complexity of the networks has become the central issue in their modeling and understanding. Hence, there is a need for sophisticated computational (algorithmic, machine learning, data mining, etc.) approaches for analyzing the networks. We develop such approaches, e.g., those for network comparison and dynamic or heterogeneous network analysis, some of which will be discussed in this talk. Also, the talk will present how our approaches can be applied to biological networks – networks of interactions between proteins in the cell, to address important problems in biomedicine, such as identifying novel aging-related genes and thus potential drug targets. In addition, the talk will show that similar approaches can be used to analyze social networks – networks of friendships or on-line social communities, to study how our friendships shape our lives, including our mental health.