K E Y N O T E S P E A K E R S
Professor & Acting Department Chair, Department of Biomedical Engineering
Professor of Electrical & Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
Aristotle University of Thessaloniki, Thessaloniki, Greece
Coordinator of i-PROGNOSIS
Title: Smartphone, Parkinson’s and Depression: A new AI-based prognostic perspective
Abstract: Machine Learning (ML) is a branch of Artificial Intelligence (AI) based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. While many ML algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster, deeper and deeper – is a recent development, leading to the realization of the so called Deep Learning (DL). The latter has an intuitive capability that is similar to biological brains. It is able to handle the inherent unpredictability and fuzziness of the natural world. In this keynote, the main aspects of ML and DL will be presented and the focus will be placed in the way they are used to shed light upon the Human Behavioral Modeling. In this vein, AI-based approaches will be presented for identifying fine-motor skills deterioration due to early Parkinson’s and depression symptoms reflected in the keystroke dynamics, while interacting with a smartphone. These approaches provide a new and unobtrusive way for gathering and analyzing dense sampled big data, contributing to further understanding disease symptoms at a very early stage, guiding personalized and targeted interventions that sustain the patient’s quality of life.
Short Bio: Leontios J. Hadjileontiadis (IEEE S’87–M’98–SM’11) was born in Kastoria (π-1966), Greece, in π-1966. He received the Diploma Degree in Electrical Engineering in 1989 and the Ph.D. Degree in Electrical and Computer Engineering in 1997, both from the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece. He also received a Diploma Degree in Musicology, AUTH, in 2011, and the Ph.D. Degree in Music Composition from the University of York, York, U.K., in 2004. His research interests include advanced signal processing, machine learning, biomedical engineering, affective computing and active and healthy ageing. His publication record includes >120 papers in peer reviewed international journals, >180 papers in peer reviewed international conference proceedings, 6 books, 2 books edited, and 24 book chapters [https://scholar.google.com/citations?user=OfAkcXkAAAAJ&hl=en]. He has a vast experience in project management, coordinating so far European and UAE projects of >US$10.000.000. Prof. Hadjileontiadis has been awarded, amongst other awards, as innovative researcher and champion faculty from Microsoft, USA (2012), the Silver Award in Teaching Delivery at the Reimagine Education Awards (2017-2018), and the Healthcare Research Award by the Dubai Healthcare City Authority Excellence Awards (2019). He is a Senior Member of IEEE.
Professor Hojjat Adeli
This speech has been cancelled due to the COVID-19 Pandemic
Ohio State University, Columbus, USA, Fellow of the Institute of Electrical and Electronics Engineers (IEEE) (IEEE), Honorary Professor, Southeast University, Nanjing, China, Member, Polish and Lithuanian Academy of Sciences, Elected corresponding member of the Spanish Royal Academy of Engineering
Title: Machine Learning: A Key Ubiquitous Technology in the 21st Century
Abstract: Machine learning (ML) is a key and increasingly pervasive technology in the 21st century. It is going to impact the way people live and work in a significant way. This lecture starts with an overview of the key ML concepts and different types of ML algorithms. In general, machine learning algorithms simulate the way brain learns and solves an estimation/recognition problem. They usually require a learning phase to discover the patterns among the available data, similar to the humans. An expanded definition of ML is advanced as algorithms that can learn from examples and data and solve seemingly interactable learning and unteachable problems, referred to as ingenious artificial intelligence (AI). Next, recent and innovative applications of ML in various fields and projects currently being pursued by leading high-tech companies such as Google, IBM, Uber, Baidu, Facebook, Pinterest, and Tesla are reviewed. Then, machine learning algorithms developed by the author and his associates are briefly described. Finally, examples are presented in different areas from health monitoring of smart highrise building structures to automated EEG-based diagnosis of various neurological and psychiatric disorders such as epilepsy, the Alzheimer’s disease, Parkinson’s disease, and autism spectrum disorder.
Short Bio: Hojjat Adeli received his Ph.D. from Stanford University in 1976 at the age of 26. He is currently an Academy Professor at The Ohio State University where he held the Abba G. Lichtenstein Professorship for ten years. He is the Editor-in-Chief of the international journals Computer-Aided Civil and Infrastructure Engineering which he founded in 1986 and Integrated Computer-Aided Engineering which he founded in 1993. He has also served as the Editor-in-Chief of the International Journal of Neural Systems since 2005. He has been an Honorary Editor, Advisory Editor, or member of the Editorial Board of 144 research journals. He has authored over 600 research and scientific publications in various fields of computer science, engineering, applied mathematics, and medicine, including 16 ground-breaking high-technology books. He is the recipient of over sixty awards and honors including three Honorary Doctorates from Lithuania, Spain, and Italy, and Honorary Professorship at several Asian and European Universities. He is a member of Academia Europaea, a corresponding member of the Spanish Royal Academy of Engineering, a foreign member of Lithuanian Academy of Sciences and Polish Academy of Science, a Distinguished Member of American Society of Civil Engineers (ASCE), and a Fellow of AAAS, IEEE, AIMBE, and American Neurological Association. He was profiled as an Engineering Legend in the journal Leadership and Management in Engineering, ASCE, April 2010, by a noted biographer of legendary engineers.
Dr. Pierre Philippe Mathieu
European Space Agency (ESA) Head of the Philab (Φ Lab) Explore Office at the European Space Agency in ESRIN (Frascati, Italy)
Title: The Rise of Artificial Intelligence for Earth Observation (AI4EO)
Abstract: The world of Earth Observation (EO) is rapidly changing as a result of exponential advances in sensor and digital technologies. The speed of change has no historical precedent. Recent decades have witnessed extraordinary developments in ICT, including the Internet, cloud computing and storage, which have all led to radically new ways to collect, distribute and analyse data about our planet. This digital revolution is also accompanied by a sensing revolution that provides an unprecedented amount of data on the state of our planet and its changes.
Europe leads this sensing revolution in space through the Copernicus initiative and the corresponding development of a family of Sentinel missions. This has enabled the global monitoring of our planet across the whole electromagnetic spectrum on an operational and sustained basis. In addition, a new trend, referred to as “New Space”, is now rapidly emerging through the increasing commoditization and commercialization of space.
These new global data sets from space lead to a far more comprehensive picture of our planet. This picture is now even more refined via data from billions of smart and inter-connected sensors referred to as the Internet of Things. Such streams of dynamic data on our planet offer new possibilities for scientists to advance our understanding of how the ocean, atmosphere, land and cryosphere operate and interact as part on an integrated Earth System. It also represents new opportunities for entrepreneurs to turn big data into new types of information services.
However, the emergence of big data creates new opportunities but also new challenges for scientists, business, data and software providers to make sense of the vast and diverse amount of data by capitalizing on powerful techniques such as Artificial Intelligence (AI). Until recently AI was mainly a restricted field occupied by experts and scientists, but today it is routinely used in everyday life without us even noticing it, in applications ranging from recommendation engines, language services, face recognition and autonomous vehicles.
The application of AI to EO data is just at its infancy, remaining mainly concentrated on computer vision applications with Very High-Resolution satellite imagery, while there are certainly many areas of Earth Science and big data mining / fusion, which could increasingly benefit from AI, leading to entire new types of value chain, scientific knowledge and innovative EO services.
This talk will present some of the ESA research / application activities and partnerships in the AI4EO field, inviting you to stimulate new ideas and collaboration to make the most of the big data and AI revolutions.
Short Bio: Pierre-Philippe is passionate about innovation and our planet: its beauty, fragility and complex dynamics as part of an integrated Earth System. His current role at ESA is to help scientists, innovators and citizens to use high-tech (such as satellite data) to better monitor, understand and preserve our home planet, by making sustainable use of its limited natural resources.
PP’s background is in Earth Sciences. He has a degree in engineering and M.Sc from the University of Liege (Belgium), a Ph.D. in climate science from the University of Louvain (Belgium), and a Management degree from the University of Reading Business School (Uk). Over the last 20 years, he has been working in environmental monitoring and modelling, across disciplines from remote sensing, modelling up to weather risk management.
Currently, PP is trying to connect the global picture we get from space with world challenges in daily life, fostering the use of our Earth Observation (EO) missions to support science, innovation and development in partnership with user communities, industry and businesses.
Department of Computer Science and Technology Jiangnan University, China
Title: Image Fusion Based on Deep Learning
Abstract: Deep Learning (DL) has found very successful applications in numerous different domains with impressive results. Image Fusion (IMF) algorithms based on DL and their applications will be presented thoroughly in this keynote lecture. Initially, a brief introductory overview of both concepts will be given. Then, IMF employing DL will be presented in terms of pixel, feature, and decision level respectively. A comprehensive analysis of DL models will be offered and their typical applications will be discussed, including Image Quality Enhancement, Facial Landmark Detection, Object Tracking, Multi-Modal Image Fusion, Video Style Transformation, and Deep Fake of Facial Images respectively.
- Sc. in Mathematics from the Nanjing Normal University, Nanjing, P.R. China in 1991.
- Sc. in 1996, and Ph.D. in “Pattern Recognition and Intelligent Systems” in 2002 both from the Nanjing University of Science and Technology, Nanjing, PR China.
- He was a fellow of United Nations University, International Institute for Software Technology (UNU/IIST) from 1999 to 2000.
- From 1996 to 2006, he taught in the School of Electronics and Information, Jiangsu University of Science and Technology where he was an exceptionally promoted professor.
- He joined School of Information Engineering (now renamed as School of IoT Engineering), Jiangnan University in 2006, where he is currently a Distinguished Professor.
- He won the most outstanding postgraduate award by the Nanjing University of Science and Technology.
- He has published more than 200 papers in his fields of research.
- He was a visiting postdoctoral researcher in the Centre for Vision, Speech, and Signal Processing (CVSSP), University of Surrey, UK from 2003 to 2004, under the supervision of Professor Josef Kittler.
- His current research interests are pattern recognition, computer vision, fuzzy systems, and neural networks. He has received several domestic and international awards, due to his research achievements.
FIEEE, FRSNZ, Fellow INNS College of Fellows, DVF RAE UK
Director, Knowledge Engineering and Discovery Research Institute,
Auckland University of Technology, Auckland, New Zealand,
Advisory/Visiting Professor SJTU and CASIA China, RGU UK
Title: Deep Learning, Knowledge Representation and Transfer with Brain-Inspired Spiking Neural Network Architectures
Abstract: The talk argues and demonstrates that the third generation of artificial neural networks, the spiking neural networks (SNN), can be used to design brain-inspired architectures that are not only capable of deep learning of temporal or spatio-temporal data, but also enabling the extraction of deep knowledge representation from the learned data. Similarly to how the brain learns time-space data, these SNN models do not need to be restricted in number of layers, neurons in each layer, etc. When a SNN model is designed to follow a brain template, knowledge transfer between humans and machines in both directions becomes possible through the creation of brain-inspired Brain-Computer Interfaces (BCI). The presented approach is illustrated on an exemplar SNN architecture NeuCube (free software and open source available from www.kedri.aut.ac.nz/neucube) and case studies of brain and environmental data modelling and knowledge representation using incremental and transfer learning algorithms These include predictive modelling of EEG and fMRI data measuring cognitive processes and response to treatment, AD prediction, BCI for neuro-rehabilitation, human-human and human-VR communication, hyper-scanning and other. More details can be found in the recent book: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer, 2019, https://www.springer.com/gp/book/9783662577134.
Short Bio: Professor Nikola Kasabov is Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK and the Scottish Computer Association. He is the Founding Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland and Professor at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology. Kasabov is the 2019 President of the Asia Pacific Neural Network Society (APNNS) and Past President of the International Neural Network Society (INNS). He is member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, Springer Series of Bio-and Neurosystems and Springer journal Evolving Systems. He is Associate Editor of several journals, including Neural Networks, IEEE TrNN, Tr CDS, Information Sciences, Applied Soft Computing. Kasabov holds MSc and PhD from TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 620 publications. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Advisory Professor at Shanghai Jiao Tong University, Visiting Professor at ETH/University of Zurich and Robert Gordon University UK. Prof. Kasabov has received a number of awards, among them: Doctor Honoris Causa from Obuda University, Budapest; INNS Ada Lovelace Meritorious Service Award; NN Best Paper Award for 2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT Medal; Honorable Member of the Bulgarian and the Greek Societies for Computer Science. More information of Prof. Kasabov can be found on the KEDRI web site: http://www.kedri.aut.ac.nz/staff.