Title: Collective decision-making in flocking animals and drones
Speaker: Prof. Tamas Vicsek (fellow of the American Physical Society and member of the Hungarian Academy of Sciences)
Prof. Tamas Vicsek is an emeritus Professor of Physics at the Biological Physics Department of Eotvos University, Budapest. Over the past 40 years, he has been involved in doing computational and experimental research on fractals, pattern formation, granular materials, collective motion (bacterial colonies, flocks, crowds, drones) and the structure and evolution of complex networks. He is a winner of several prestigious prizes and awards. In addition, he has had visiting research positions and professorships at various research institutes and universities, including Emory University, Yale University and the University of Notre Dame.
Keynote abstract: Flocking is a typical phenomenon involving near instantaneous decisions by many units. The natural world - and very recently, some technological applications - are full of cohesive collectives moving together with high efficiency. Schools of fish or flocks of birds maintain their global direction despite even significant noise perturbing the individuals, yet they perform abrupt collective turns when relevant agitation alters the state of a few members. Ruling local fluctuations out of global movement leads to persistence and needs overdamped interaction-dynamics, while propagating swift turns throughout the group leads to responsivity and needs underdamped interaction-dynamics. Thus, the two aspects, i) presistence nad ii) responsibility appear to be acting against each other. Evolution found a way out of this conflict for higher order social species needing the above features for their survival. We argue that individuals are pushed to develop some kind of cognitive capabilities or local signalling in order to let the collective find the best method to optimize the persistence-responsivity tradeoff.
In this talk, we introduce a simple concept that mimics such cognition-based collective behaviour and demonstrate its feasibility by integrating it into standard agent-based models of collective motion. We construct a time-dependent, adaptive leadership hierarchy that is driven by the clear intention (will) of agents to change direction when they receive relevant new information about the environment. We show that adding this will-based hierarchy to the most relevant models of collective motion highly enhances the responsivity of the flock and thus enables breaking out of the former limits of the persistence-responsivity tradeoff. We show that the increased responsivity scales well with growing flock sizes. As a consequence, this enables the construction of very large swarms without significant density fluctuations, which is a cornerstone of stability and safety of such systems in nature. Finally, we also demonstrate that this finding on the level of principles easily translates to practical developments by surpassing state-of-the-art solutions of moving large numbers of artificial agents in a closed space with high speed and coherence.
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.