Title: AI-Enabled Robotics
Speaker: Prof. Frank Kirchner is the executive director of the DFKI Bremen and in charge of the research department „Robotics Innovation Center“ with more than 100 employees.
Founded in 2006 as a DFKI laboratory in Bremen, the institute is building upon the fundamental research of the Robotics Research Group led by Kirchner at the University of Bremen. Since 2002, the university professor has held the chair for robotics in the faculty of mathematics and computer science. Kirchner studied and obtained his doctorate at the University of Bonn and worked as a senior researcher at the Gesellschaft für Mathematik und Datenverarbeitung (GMD) in Sankt Augustin and at the Faculty of Electrical Engineering at Northeastern University in Boston (USA). In 2013, under Kirchner’s leadership and following the model of the DFKI, the Brazilian government founded the "Brazilian Institute of Robotics" in Salvador da Bahia, where he was the Scientific Director during the development phase. There he was awarded an honorary doctorate in 2017 for his achievements in the field of robotics and artificial intelligence. Frank Kirchner is one of the leading experts in the field of biologically inspired behavior and motion sequences of highly redundant, multifunctional robot systems. In addition, he supervises a variety of doctoral theses, is a regular reviewer for a number of international scientific journals and conferences and is the author of more than 350 publications in the field of robotics and AI. In 2015, Frank Kirchner was elected and accepted as a member of the Berlin Brandenburg Academy of Sciences and Humanities (BBAW).Keynote abstract: In recent years robotics and artificial intelligence has gained a lot of interest also in the area of production and manufacturing. While systems for a long time have been used as tools to implement classical AI approaches in the area of object recognition, environment representation, path and motion planning etc., researchers now begin to understand that the robot itself is part of the question and has to be taken into account when thinking about AI. Robotics and AI in production and manufacturing scenarios ultimately requires for intelligent systems that come with a degree of structural (kinematic) complexity that allows them to perform on the same level as their human counterparts. This talk will survey the state of the art in robotics and outline ways to tackle the question of AI in the light of the systems as an integral part of the approach. Future milestones and key achievements will be discussed and a scenario will be presented on how to integrate modern robotic systems – that provide the above mentioned AI features and electromechanical
Title: to be announced
Speaker: Prof. Dirk Helbing
Prof. Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences and affiliate of the Computer Science Department at ETH Zurich. In January 2014 Prof. Helbing received an honorary PhD from Delft University of Technology (TU Delft). Since June 2015 he is affiliate professor at the faculty of Technology, Policy and Management at TU Delft, where he leads the PhD school in "Engineering Social Technologies for a Responsible Digital Future".
Dirk Helbing started as a physicist. With his diploma thesis, he initiated the area of pedestrian, crowd, and evacuation modeling and simulation. During his PhD and habilitation in physics, he helped to establish the fields of socio-, econo- and traffic physics. He was also co-founder of the Physics of Socio-Economic Systems Division of the German Physical Society (DPG). As a visiting scientist at Tel Aviv University and the Weizmann Institute in Israel, the Eötvös University in Budapest, and Xerox PARC in California, he focused on various complex systems - from panicking pedestrians to traffic jams, and from bacterial patterns to La Ola waves. At Dresden University of Technology he became the Managing Director of the Institute of Transport & Economics, worked on traffic assistant systems (i.e. early self-driving cars) and a self-organized traffic light control system, which got patented. He discovered that crowd disasters are caused by a phenomenon called crowd turbulence and worked on ways to describe, reduce and respond to disasters. As professor of Sociology at ETH Zurich, he worked on evolutionary game theory and agent-based computer simulations of social processes and phenomena.
The work of Prof. Helbing is documented by hundreds of media reports and publications, among them more than 10 papers in Nature, Science, and PNAS. He won various prizes, including the Idee Suisse Award. He co-founded the Competence Center for Coping with Crises in Complex SocioEconomic Systems, the Risk Center, the Institute for Science, Technology and Policy (ISTP) and the Decision Science Laboratory (DeSciL). While coordinating the FuturICT initiative (www.futurict.eu), he helped to establish data science and computational social science in Europe, as well as global systems science. A further result is the Nervousnet platform (nervousnet.info). Helbing is an elected member of the German Academy of Sciences "Leopoldina" and the World Academy of Art and Science. He worked for the World Economic Forum’s Global Agenda Council on Complex Systems. He was elected member of the External Faculty of the Santa Fe Institute and now belongs to the External Faculty of the Complexity Science Hub Vienna. He sits in the Boards of the Global Brain Institute in Brussels and the International Centre for Earth Simulation in Geneva. Recently, he is also involved in the area of Citizen Science, the activities of the "Staatslabor" (a Swiss gov.lab) as well as the establishment of the Blockchain [X] initiative and the Blockchain Lab in Delft. Last but not least, he is also a member of federal and academy-of-science committees addressing the digital transformation of our society.
Title: to be announced
Speaker: Prof. Guanrong Chen (City University of Hong Kong, China)
Guanrong Chen (M’89, SM’92, F’97, LF’19) received the MSc degree in Computer Science from Sun Yat-sen University, Guangzhou, China in 1981 and the PhD degree in Applied Mathematics from Texas A&M University, College Station, Texas in 1987. He has been a Chair Professor and the Founding Director of the Centre for Chaos and Complex Networks at the City University of Hong Kong since year 2000, prior to that he was a tenured Full Professor at the University of Houston, Texas, USA. He was awarded the 2011 Euler Gold Medal, Russia, and conferred Honorary Doctorate by the Saint Petersburg State University, Russia in 2011 and by the University of Le Havre, Normandy, France in 2014. He is a Member of the Academy of Europe and a Fellow of The World Academy of Sciences, and has been a Highly Cited Researcher in Engineering according to Thomson Reuters since 2009.
Title: to be announced
Speaker: Prof. Dr. Hanspeter A. Mallot
Prof. Dr. Hanspeter A. Mallot is Professor of Cognitive Neuroscience at the Eberhard Karls University, Tübingen. Research focusses on spatial cognition in humans and robots, using behavioral experiments in virtual reality, eye-movement recordings, and simulated agents in hard- and software.
He is a member of the editorial board of the journal "Spatial Cognition and Computation" and of the "Neurowissenschaftliche Gesellschaft" (NWG). In the past, he served as president of the European Neural Network Society (ENNS), as president of the German Society for Cognitive Science (GK), and as a member of the Neuroscience review panel of the Deutsche Forschungsgemeinschaft.
Title: Smart Medicine: Medical Big Data Mining /AI with Innovative Applications in Patient Monitoring, Diagnosis, Prediction and Health Management
Speaker: Prof Yanchun Zhang
Prof. Yanchun Zhang is a Professor and Director of Centre for Applied Informatics at Victoria University, and is currently an adjunct professor at Guangzhou University. Dr Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases, data mining, web services and e-health. He has published over 300 research papers in international journals and conference proceedings including ACM Transactions on Computer and Human Interaction (TOCHI), IEEE Transactions on Knowledge and Data Engineering (TKDE), VLDBJ, SIGMOD and ICDE conferences, and a dozen of books and journal special issues in the related areas. Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer), and also the founding editor of Web Information Systems Engineering Book Series and Health Information Science Book Series. He is Chairman of International Web information Systems Engineering Society (WISE). He was a member of Australian Research Council's College of Experts (2008-2010), and also serves as expert panel member at various international funding agencies such as National Natural Science Fund of China (NSFC), “National 1000 Talents Program” of China, the Royal Society of New Zealand Marsden Fund and National Natural Science Fund of China (NSFC).
Keynote abstract: Due to the recent development or maturation of database, data storage, data capturing, and sensor technologies, huge medical and health data have been generated at hospitals and medical organizations at unprecedented speed. Those data are a very valuable resource for improving health delivery, health care and decision making and better risk analysis and diagnosis. Health care and medical service is now becoming more data-intensive and evidence-based since electronic health records are used to track individuals' and communities' health information (particularly changes). These substantially motivate and advance the emergence and the progress of data-centric health data and knowledge management research and practice.
In this talk, we will introduce several innovative data mining techniques and case studies to address the challenges encountered in e-health and medical big data. This includes techniques and development on medical data streams, correlation analysis, abnormally detection and risk predictions with patient monitoring and aging care applications.