Invited Keynote Speakers

Victor Lesser

Founding Fellow of AAAI, IEEE Fellow
Founding Director, Multi-Agent Systems Laboratory
Distinguished Professor Emeritus,
University of Massachusetts Amherst, USA

Keynote Title: Coordination in the Small and Large for Expert Crowd Sourcing: Overview of MAS Techniques for Coordination of Complex Tasks

Abstract: Crowd sourcing involving complex tasks and experts will require new crowd sourcing frameworks in order for effective automated task management. Multi-agent systems have a lot of similarity with this type of crowd sourcing, and hopefully there can be a cross fertilization between disciplines. I will present a number of approaches that my group has developed over the years to coordinate software agents performing complex problem-solving tasks. I will discuss coordination from the perspective of both small group problem solving and large organizations.

Speaker’s Bio: Victor Lesser received the Ph.D. degree in Computer Science from Stanford University, Stanford, CA, 1973. He is an Emeritus Distinguished Professor of Computer Science and Founding Director of the Multi-Agent Systems Laboratory in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. His major research focus is on the control and organization of complex AI systems. He has pioneered work in the development of a wide variety of techniques for the coordination of and negotiation among multiple software agents. He is considered one of the founders of the Multi-Agent field starting with his early work in 1978. He has also made contributions in the areas of real-time control, self-aware control, machine learning, signal understanding, diagnostics, plan recognition, and computer-supported cooperative work. He has worked in application areas such as sensor networks for vehicle tracking and weather monitoring, speech and sound understanding, information gathering on the internet, peer-to-peer information retrieval, intelligent user interfaces, distributed task allocation and scheduling, and virtual agent enterprises. In terms of statistics, he has published over 500 papers, graduated 36 PhD students, and based on Google Scholar his citation count is 28000, h-index is 82 and i10-index is 307. A number of his former students are internationally recognized AI scholars in the highest tier of their age cohorts.

Professor Lesser’s research accomplishments have been recognized by many major awards over the years. He received the IJCAI-09 Award for Research Excellence, the most prestigious award in AI. He is also a Founding Fellow of AAAI and an IEEE Fellow. He was General Chair of the first international conference on Multi-Agent Systems (ICMAS) in 1995 and Founding President of the International Foundation of Autonomous Agents and Multi-Agent Systems (IFAAMAS). In 2007, to honor his contributions to the field of multi-agent systems, IFAAMAS established the “Victor Lesser Distinguished Dissertation Award.” He also received a Special Recognition Award for his foundational research in generalized coordination technologies from the Information Processing Technology Office at DARPA.

Ke Wang

Professor, School of Computer Science
Simon Fraser University, Canada

Keynote Title: Data Mining vs Data Privacy

Abstract: Big data/data access are essential for developing intelligent solutions for business and organizations. Privacy regulations, on the other hand, state that researchers should first strip any protected sensitive information. Without privacy preserving techniques, data cannot be safely shared and the benefits of big data and AI cannot be realized. I will talk about the dilemma between data mining and data privacy, and various privacy preserving techniques that enable the benefits of data mining while meeting privacy regulations.

Speaker’s Bio: Ke Wang received Ph.D from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Ke Wang’s research interests include database technology, data mining and knowledge discovery, with emphasis on massive datasets, graph and network data, and data privacy. Ke Wang has published in database, information retrieval, and data mining conferences, including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM, ICDM, WWW, AAAI, and CIKM. He has 40+ papers that receive 100+ citations each. He co-authored a book “Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques”, Data Mining and Knowledge Discovery Series, Chapman & Hall/CRC, August 2010. He was an associate editor of the ACM TKDD journal, an associate editor of the IEEE TKDE journal, an editorial board member for Journal of Data Mining and Knowledge Discovery.