Keynote Speakers


Keynote Speakers:

  • Philip S. Yu, University of Illinois at Chicago, USA

  • Milan Zeleny, The ZET Foundation and The Tomas Bata University, USA

  • Fuad Aleskerov, National Research University Higher School of Economics and Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russia

  • James M. TIEN, College of Engineering, University of Miami, Florida, USA

  • Richard C. Larson, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA

On Fusion of Heterogeneous Data Sources

Philip S. YU
Professor, University of Illinois at Chicago, USA


The problem of big data has become increasingly importance in recent years. On the one hand, big data is an asset that potentially can offer tremendous value or reward to the data owners. On the other hand, it poses tremendous challenges to distil the value out of the big data. The very nature of big data poses challenges not only due to its volume, and velocity of being generated, but also its variety, where variety means the data can be collected from various sources with different formats from structured data to text to network/graph data, etc. In this talk, we focus on the variety issue and discuss the recent development in fusion of information from multiple data sources, which can be applied to multiple applications and disciplines. As the number and variety of social networks aimed at different purposes increase rapidly, users nowadays are participated in multiple online networks simultaneously to enjoy various services. How to fuse information spreading across multiple networks to achieve better understanding of customers and provide higher quality of services becomes the Holy Grail. Social networks will be used as an example to explain how to address the data fusion issue.

Speaker’s  short biographical note:

Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society¡¯s 2013 Technical Achievement Award for ¡°pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data¡±, and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,000 referred conference and journals papers cited more than 80,000 times with an H-index of 131. He has received more than 300 patents.
Dr. Yu is the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data. He is on the steering committee of ACM Conference on Information and Knowledge Management and was a steering committee member of the IEEE Data Engineering and the IEEE Data Mining Conference. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He received the ICDM 2013 10-year Highest-Impact Paper Award, and the EDBT Test of Time Award (2014). He had received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 94th plateau of Invention Achievement Awards. He was an IBM Master Inventor. Dr. Yu received his PhD from Stanford University.


Entering the Age of Acceleration: From Information to Knowledge, From Description to Entrepreneurial Action

Milan Zeleny
Professor, The ZET Foundation and The Tomas Bata University


We are entering an era of rapid change acceleration in economics, politics, technology, education, entrepreneurship and social organization. The speed of change has become a significant quality of its own. Increasing speed affects the world around us: we can no longer forecast, wait or delay action. As information becomes obsolete much faster, we have to increasingly operate within the space of (entrepreneurial) action. Learning how to act in an environment of continuous change is a major challenge for individuals, groups, companies and institutions. The division of labor between those who think and those who do, between theory and application or between description and action, cannot be sustained at the times of accelerated change.

Speaker’s  short biographical note:

Milan Zeleny is Professor (Emer.) of Management Systems at Fordham University. His previous appointments were at Columbia University, Univ. of So. Carolina, Copenhagen Sch. of Economics, European Inst. for Advanced Studies, and U. of Rochester. Major visiting appointments include Beijing, Xi¡äan, Tianjin, Taipei, Shanghai, Christchurch, Pretoria, Tokyo, Naples, Padua, Rome, Milan, Rio de Janeiro, Perth, Kanpur, Melbourne, Zl¨ªn, Turku, Helsinki, etc. He has become a global actor through his ZET ¨C Foundation.
Zeleny¡¯s scientific interests have always been always multi- and trans-disciplinary. His most significant contributions are in the areas of multiple criteria decision making (MCDM), autopoiesis, fuzzy sets, artificial life, synthetic biology, multiobjective programming, games with multiple payoffs, De novo programming, conflict dissolution, Bata management system, tradeoffs-free economics, evolutionary economics, strategy as action, economic transformation and metamorphosis, etc. Naturally, his publications, although well above six hundred, are not rooted in traditional specialization, but in an ever-expanding array of interactions of mutually synergetic and merging areas of interest.

Power Indices in Networks and their Applications

National Research University Higher School of Economics and Institute of Control Sciences of Russian Academy of Sciences


We consider an application of power indices which take into account preferences of agents for coalition formation proposed for an analysis of power distribution in elected bodies to reveal most powerful (central) nodes in networks. These approach allows to take into account the parameters of the nodes in networks, a possibility of group influence from the subset of nodes to single nodes, and intensity of short and long interactions among the nodes. Some properties of the indices are discussed.
Various applications are presented - migration, foreign claims, international conflicts, food export/import, ranking of journals on the basis of cross-citation, etc.

Speaker’s  short biographical note:

FUAD ALESKEROV is the head of Department of Mathematics for Economics, National Research University Higher School of Economics, the head of International Laboratory of Decision Analysis and Choice, National Research University Higher School of Economics and the head of Laboratory of Choice Theory and Decision Analysis, Russian Academy of Sciences, Institute of Control Sciences. He got the PhD degree in Control in Socio--Economic Systems at 1981. He has published 10 books, more than 200 articles, more than 100 in peer-reviewed journals and volumes.
He is the member of The Society for Social Choice and Welfare (member of the Council, 2008-2013), International Economic Association (member of the Executive Council, 2011-2017), American Mathematical Society and New Economic Association, Russia. He also participanted more than 70 conferences and workshops as the invited speaker.


Internet of Things, Real-Time Decision Making and Artificial Intelligence

James M. TIEN
Professor, College of Engineering, University of Miami, USA


In several earlier papers, the author defined and detailed the concept of a servgood, which can be thought of as a physical good or product enveloped by a services-oriented layer that makes the good smarter or more adaptable and customizable for a particular use. Adding another layer of physical sensors could then enhance its smartness and intelligence, especially if it were to be connected with other servgoods ¨C thus, constituting an Internet of Things (IoT) or servgoods. More importantly, real-time decision making is central to the Internet of Things; it is about decision informatics and embraces the advanced technologies of sensing (i.e., Big Data), processing (i.e., real-time analytics), reacting (i.e., real-time decision-making), and learning (i.e., deep learning). Indeed, real-time decision making (RTDM) is becoming an integral aspect of IoT and artificial intelligence (AI), including its improving abilities at voice and video recognition, speech and predictive synthesis, and language and social-media understanding. These three key and mutually supportive technologies ¨C IoT, RTDM, and AI ¨C are considered herein, including their progress to date.

Speaker’s  short biographical note:

James M. Tien received the BEE from Rensselaer Polytechnic Institute (RPI) and the SM, EE and PhD from the Massachusetts Institute of Technology. He has held leadership positions at Bell Telephone Laboratories, at the Rand Corporation, and at Structured Decisions Corporation. He joined the Department of Electrical, Computer and Systems Engineering at RPI in 1977, became Acting Chair of the department, joined a unique interdisciplinary Department of Decision Sciences and Engineering Systems as its founding Chair, and twice served as the Acting Dean of Engineering. In 2007, he was recruited by the University of Miami to be a Distinguished Professor and Dean of its College of Engineering; effective 2016, he stepped down from the Dean¡¯s position and remains a Distinguished Professor. He has been awarded the IEEE Joseph G. Wohl Outstanding Career Award, the IEEE Major Educational Innovation Award, the IEEE Norbert Wiener Award, and the IBM Faculty Award. He is also an elected member of the prestigious U. S. National Academy of Engineering.


Queues in Service Systems: Some Unusual Applications and New IT-Facilitated Methodologies

Richard C. Larson
Professor,Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA


Virtually all service systems have queues. Traditionally, a queue is a line of standing customers waiting for service, and the queue exists because near-term demand for service exceeds existing capacity to provide service. A system¡¯s queue performance is usually a major factor in evaluating total system quality. Queues can exist in unusual places, often very different from the traditional standing line of customers. Here we visit some of these less obvious queues, and we present new queue analysis methodologies made possible by IT technologies.
Our tour of queues will include: (1) the Hypercube Queue model for emergency services such as ambulance and police services; (2) queues of PhD¡¯s waiting as postdoctoral fellows, hoping to obtain a tenure-track faculty position; (3) a university¡¯s faculty as a large queue, where ¡°service¡± is leaving the academic ranks; (4) queues of callers attempting to reach local emergency numbers; (5) queues of moving cars trying to find inexpensive on-street parking in cities. In addition, we will present two non-parametric queue inference methodologies made possible by IT technologies. These are the Queue Inference Engine (QIE), that accurately estimates transient queue performance only from transactional data, and a method to estimate the probability distribution of total queue delay from only sampling queue dwellers and asking each, ¡°How long have you been waiting in queue?¡±


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