Professor Jean-Jacques Slotine
Non Linear Systems Lab, Massachusetts Institute of Technology, USA
Title: "Collective computation in adaptive nonlinear networks and the grammar of evolvability"
Short Biography: Professor Jean-Jacques Slotine received his Ph.D. from the Massachusetts Institute of Technology in 1983, at age 23. After working at Bell Labs in the computer research department, he joined the faculty at MIT in 1984, where he is currently Professor of Mechanical Engineering and Information Sciences, Professor of Brain and Cognitive Sciences, and Director of the Nonlinear Systems Laboratory. He teaches and conducts research in the areas of dynamic systems, robotics, control theory, computational neuroscience, and systems biology.
Research in Professor Slotine's laboratory focuses on developing rigorous but practical tools for nonlinear systems analysis and control. These have included key advances and experimental demonstrations in the contexts of sliding control, adaptive nonlinear control, adaptive robotics, machine learning, and contraction analysis of nonlinear dynamical systems.
Professor Slotine is the co-author of two popular graduate textbooks, "Robot Analysis and Control" (Asada and Slotine, Wiley, 1986), and "Applied Nonlinear Control" (Slotine and Li, Prentice-Hall, 1991) and is one of the most cited researcher in both systems science and robotics. He was a member of the French National Science Council from 1997 to 2002, and a member of Singapore's A*STAR SigN Advisory Board from 2007 t0 2010. He is currently a member of the Scientific Advisory Board of the Italian Institute of Technology. He has held Invited Professor positions at College de France, Ecole Polytechnique, Ecole Normale Superieure, Universita di Roma La Sapienza, and ETH Zurich. He is the recipient of the 2016 Rufus Oldenburger Medal.
Research areas: Brain Sciences, Complex Systems, Dynamical Systems, Control Theory, Computational Neuroscience, Robotics
Professor Anastasios Tefas
Aristotle University of Thessaloniki, Department of Informatics, Greece
Title: "Deep Learning and Robotics: perception, control and innovations"
Abstract: This keynote speech will focus on deep learning methods and their use in robotics for increased perception, control and other innovative tasks. Deep learning emerged as one of the most promising research fields in artificial intelligence. The significant advancements that deep learning methods have brought out for large scale image classification tasks have generated a surge of excitement in applying the techniques to other problems in computer vision and more broadly into other disciplines of computer science, such as robotics. However, building deep learning algorithms for highly non-linear real-world problems such as those encountered in computer vision and robotics is non-trivial and requires substantial expertise. Unmanned Aerial Vehicles (drones) are among the robotic units that have substantial needs for autonomous control and perception due to their increasing use in several applications like transportation, inspection, surveillance and cinematography among others. Deep Convolutional Neural Networks (CNNs) are among the state-of-the-art techniques for Visual Information Analysis that can provide increased perception capabilities. CNNs can be used to perform several robotic perception tasks such as object detection and tracking, face detection and person identification, crowd detection for ensuring flight safety on drones, emergency landing point detection, etc. However, deploying such deep learning models on drones or other robotic units is not a straightforward task, since there are significant memory and model complexity constraints. To overcome these limitations several methodologies have been proposed: a)training small lightweight CNNs, b)using knowledge transfer techniques, such as neural-network distillation, layer hints and similarity embeddings, to reduce the size of CNNs and c) using neural region proposals for fast object detection and classification (faster R-CNN, YOLO, SSD).
Furthermore, gathering training data suitable for training the deep learning models is also a challenging task. Learning by using dataset augmentation techniques, such as hard negative and positive sample mining, can help to partially overcome this limitation, while allows us to further increase the performance of the trained models. Deep learning techniques can be also used for end-to-end drone control, allowing the deep model to control every aspect of the flight, from the visual information analysis to the drone and camera controls. Using multiple drones (multidrone setup) can increase the flexibility of drone cinematography. Such systems can be adaptive and evolving. Finally, there are several deep learning frameworks that can be used for deploying the aforementioned deep learning techniques on robotics (Tensorflow, Caffe, Theano, Darknet, etc).
Short Biography: Anastasios Tefas received the B.Sc. in informatics in 1997 and the Ph.D. degree in informatics in 2002, both from the Aristotle University of Thessaloniki, Greece. Since 2017 he has been an Associate Professor at the Department of Informatics, Aristotle University of Thessaloniki. From 2008 to 2017, he was a Lecturer, Assistant Professor at the same University. From 2006 to 2008, he was an Assistant Professor at the Department of Information Management, Technological Institute of Kavala. From 2003 to 2004, he was a temporary lecturer in the Department of Informatics, University of Thessaloniki. From 1997 to 2002, he was a researcher and teaching assistant in the Department of Informatics, University of Thessaloniki. Dr. Tefas participated in 12 research projects financed by national and European funds. He has co-authored 76 journal papers, 188 papers in international conferences and contributed 8 chapters to edited books in his area of expertise. Over 3900 citations have been recorded to his publications and his H-index is 33 according to Google scholar. His current research interests include computational intelligence, deep learning, pattern recognition, statistical machine learning, robotics, digital signal and image analysis and retrieval and computer vision.
Professor Plamen Angelov
Lancaster University, United Kingdom
Title: To be confirmed
Profile: Prof. Angelov (MEng 1989, PhD 1993, DSc 2015) is a Fellow of the IEEE, of the IET and of the HEA. He is Vice President of the International Neural Networks Society (INNS) for Conference and Governor of the Systems, Man and Cybernetics Society of the IEEE. He has 25+ years of professional experience in high level research and holds a Personal Chair in Intelligent Systems at Lancaster University, UK. He leads the Data Science group at the School of Computing and Communications which includes over 20 academics, researchers and PhD students. He has authored or co-authored 300 peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 6 patents, two research monographs (by Wiley, 2012 and Springer, 2002) cited over 6200+ times with an h-index of 38 and i10-index of 110. His single most cited paper has 810 citations. He has an active research portfolio in the area of computational intelligence and 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 (including several multimillion ones) funded by UK research councils, EU, industry, UK MoD. 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 co-Editor-in-Chief of Springer's journal on Evolving Systems and Associate Editor of several leading international scientific journals, including IEEE Transactions on Fuzzy Systems (the IEEE Transactions with the highest impact factor) of the IEEE Transactions on Systems, Man and Cybernetics as well as of several other journals such as Applied Soft Computing, Fuzzy Sets and Systems, Soft Computing, etc. He gave over a dozen plenary and key note talks at high profile conferences. Prof. Angelov was General co-Chair of a number of high profile conferences including IJCNN2013, Dallas, TX; IJCNN2015, Killarney, Ireland; the inaugural INNS Conference on Big Data, San Francisco; the 2nd INNS Conference on Big Data, Thessaloniki, Greece and a series of annual IEEE Symposia on Evolving and Adaptive Intelligent Systems. Dr Angelov is the founding Chair of the Technical Committee on Evolving Intelligent Systems, SMC Society of the IEEE and was previously chairing the Standards Committee of the Computational Intelligent Society of the IEEE (2010-2012). He was also a member of International Program Committee of over 100 international conferences (primarily IEEE).