Raphael Jungers is a FNRS Professor at UCLouvain, Belgium. His main interests lie in the fields of Computer Science, Graph Theory, Optimization and Control. He received a Ph.D. in Mathematical Engineering from UCLouvain (2008), and a M.Sc. in Applied Mathematics, both from the Ecole Centrale Paris, (2004), and from UCLouvain (2005).

He has held various invited positions, at the Université Libre de Bruxelles (2008-2009), at the Laboratory for Information and Decision Systems of the Massachusetts Institute of Technology (2009-2010), at the University of L´Aquila (2011, 2013, 2016), and at the University of California Los Angeles (2016-2017).

He is a FNRS, BAEF, and Fulbright fellow. He has been an Associate Editor for the IEEE CSS Conference Editorial Board, and the journals NAHS, Systems and Control Letters, and IEEE Transactions on Automatic Control. He was the recipient of the IBM Belgium 2009 award and a finalist of the ERCIM Cor Baayen award 2011. He was the co-recipient of the SICON best paper award 2013-2014.

Title: Algebraic and Optimization techniques for Cyber-Physical systems

Modern control systems are more and more complex. Not only are they impacted by increasingly complicated and multiple constraints (sustainability, privacy, security, resilence, etc.), they are also subject to the increasingly complex nature of computation technology (embedded, decentralized, hybrid, crowdsourced,...). Such systems are often coined under the name of Cyber-Physical systems. I will survey several mathematical techniques that allow to cope with these arising difficulties and at the same time leverage the new opportunities of Cyber-Physical Systems. I will focus on two of them: Path-Complete Lyapunov functions and Chance-Constrained Optimization.

Muhammad Imran is the Vice Dean Glasgow College UESTC and Professor of Communication Systems in the School of Engineering at the University of Glasgow. He was awarded his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, U.K., in 2002 and 2007, respectively. He is an Affiliate Professor at the University of Oklahoma, USA and a visiting Professor at 5G Innovation Centre, University of Surrey, UK. He has over 18 years of combined academic and industry experience, working primarily in the research areas of cellular communication systems. He has been awarded 15 patents, has authored/co-authored over 300 journal and conference publications, and has been principal/co-principal investigator on over GBP 6 million in sponsored research grants and contracts. He has supervised 30+ successful PhD graduates.

He has an award of excellence in recognition of his academic achievements, conferred by the President of Pakistan. He was also awarded IEEE Comsoc's Fred Ellersick award 2014, FEPS Learning and Teaching award 2014, Sentinel of Science Award 2016. He was twice nominated for Tony Jean's Inspirational Teaching award. He is a shortlisted finalist for The Wharton-QS Stars Awards 2014, QS Stars Reimagine Education Award 2016 for innovative teaching and VC's learning and teaching award in University of Surrey. He is a senior member of IEEE and a Senior Fellow of Higher Education Academy (SFHEA), UK.

Title: Communication Systems to Combat Emergency and Disaster Scenarios - A 5G use case

The role of communication technologies in the advent of disasters and natural calamities is significant beyond any doubts. However, in the advent of any such incident the wireless communication infrastructure is also partially destroyed and there is a need to set up a wireless communication network using the principles of self-organised networking. In this seminar, we will review the role of SON for ad hoc networking and then focus on a specific use case of SON for application-aware joint RAN and backhaul optimisation.

Damien Coyle, Professor of Neurotechnology, is currently Director Intelligent Systems Research Centre and Research Director in the School of Computing, Engineering and Intelligent Systems at Ulster University. He has published over 130 research papers in areas such as computational intelligence/AI, bio-signal processing, computational neuroscience, neuroimaging, neurotechnology and brain-computer interface (BCI) applications and has won a number of prestigious international awards for his research including the 2008 IEEE Computational Intelligence Society (CIS) Outstanding Doctoral Dissertation Award and the 2011 International Neural Network Society (INNS) Young Investigator of the Year Award. He was an Ulster University Distinguished Research Fellow in 2011, a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow in 2013 and a Royal Academy of Engineering Enterprise Fellow in 2016-2017. He is a founding member of the International Brain-Computer Interface Society, a Senior member of the IEEE and chairs the IEEE Computational Intelligence Society (CIS) UKIreland chapter.

Professor Coyle is also CEO of NeuroCONCISE Ltd, the Ulster University spinout company he founded in 2016 to build wearable neurotechnology that non-invasively measures and translates brainwaves into control signals using advanced algorithms to enable people to interact with technology and communicate without moving which has applications in rehabilitation, diagnostics, augmentative and assistive communication devices and entertainment.

Title : Brain-computer interfaces and wearable neurotechnology: Lab experiments to real world applications

Since the beginning of the 21st century, research in the field of brain-computer interfaces (BCIs) and neurotechnology has proven that electrical signals in the brain, modulated intentionally by thinking or imagining, can relay information directly to a computer, where it is translated by intelligent algorithms (some inspired by the brain's neural networks) into control signals that enable communication without movement. People with restricted abilities caused by disease or injury may benefit from this technology, for example, those who have locked-in syndrome following traumatic brain injury and are unable to communicate. Professor Coyle's neurotechnology research and development at Ulster University is not only targeting restoration or replacement of natural central nervous system (CNS) functions for people who most need assistive technology, but to enhance, improve and supplement CNS function for everyone, to train and entertain with brain controlled computer games and even enable ‘cybathletes' to compete in the Cybathlon championship for athletes with disabilities.

This talk will provide an overview of Professor Coyle's experience in these areas and the challenges associated with translating research outcomes intoimpactful wearable and medical devices that may improve the quality of life for many. Recent fundamental research aimed at decoding imagined 3D arm movements from electroencephalography (EEG), emotion inducing imagery, decoding imagined speech and classifying imagined 3D shapes from EEG will be also be presented.

Coorous Mohtadi is the EMEA Manager of MathWorks technical specialist team supporting universities focusing on the application of MATLAB and Simulink in laboratories and curriculum development. He is interested in finding synergies between industry and academia. He has been supporting research, design and development in universities and industry for the last 11 years at MathWorks.

Prior to joining MathWorks in 2007, Coorous was the European technical manager for temperature, process control, and component products at Omron Electronics Europe and the chief control engineer at Eurotherm Controls. During 1980s he was also a postdoctoral research fellow at University of Oxford, U.K. and University of Alberta, Canada. Coorous holds a D.Phil. in model-based predictive control and Masters in engineering science, both from University of Oxford. His paper on generalised predictive control has over 3500 citations and his industrial algorithms forms the core of extremely successful Eurotherm Controls 2000 series of products

Title: Deep Learning with MATLAB: Real-time Object Recognition and Transfer Learning

Deep learning can achieve state-of-the-art accuracy for many tasks considered algorithmically unsolvable using traditional machine learning, including classifying objects in a scene or recognizing optimal paths in an environment. This presentation demonstrates a practical approach to the domain of deep learning and enables discovery of new MATLAB® features that simplify these tasks and eliminate the low-level programming. Use of pre-trained networks and transfer learning is discussed. The tools enable swift transition from prototype to production, build and train neural networks and automatically convert a model to CUDA® to run natively on GPUs.