Plenary Panels
Plenary Panel: Cognitive Machine Intelligence and Killer Applications
Wednesday, Dec. 2ed, 2020 (US EST, GMT-5) 12:30 PM - 2:30 PM (US EST, GMT-5) Zoom Meeting
Cognitive intelligence often refers to the machine intelligence that can augment human cognitive functions, possibly with continued interactions and feedbacks with human. In the recent five years, we have witnessed rapid deployments of cognitive intelligence in business, science and engineering applications. This panel consists of distinguished experts in AI algorithms, AI systems, and AI applications. They are the leaders in developing and deploying cognitive intelligence in cyber-physical systems, education, social media, medicine and representation learning. The panelists will share their points of view in terms of killer applications and research challenges in cognitive machine intelligence and debate on how cognitive intelligence technologies may impact on the cyber, social and physical world in which we live, work and interact.
Panel Moderator
Huan Liu
ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, Member of ASEE and SIAMProfessor, Arizona State University, USA
Bio - Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is a co-author of a text, Social Media Mining: An Introduction, Cambridge University Press. He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, and Chief Editor of Data Mining and Management in Frontiers in Big Data. He is a Fellow of ACM, AAAI, AAAS, and IEEE. More can be found at his home page.
Panelists(alphabetical by last name)
Tarek Abdelzaher
Professor and Willett Faculty Scholar, University of Illinois at Urbana Champaign, USAACM Fellow
Bio - Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in real-time computing, distributed systems, IoT, sensor networks, and control. He served an Editor-in-Chief of the Journal of Real-Time Systems, and as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, the ACM Transaction on Sensor Networks, the ACM Transactions on the Internet of Things, the ACM Transactions on Internet Technology, and the Ad Hoc Networks Journal, among others. He chaired (as Program or General Chair) several conferences in his area including RTAS, RTSS, IPSN, Sensys, DCoSS, ICDCS, and ICAC. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as several best paper awards. He is a senior member of IEEE and a fellow of ACM.
Paolo Boldi
Professor, University of Milano, ItalyBio - Paolo Boldi is full Professor at the Università degli Studi di Milano from 2015, where he is currently the co-ordinator of the PhD Program in Computer Science. His main research topics are algorithms and data structures for big data, web crawling and indexing, graph compression, succinct and quasi-succinct data structures, distributed systems, anonymity and alternative models of computation. Recently, his works focused on problems related to complex networks (especially, the World-Wide Web, social networks and biological networks), a field where his research has also produced software tools used by many people working in the same area. He chaired many important conferences in this sector (e.g., WSDM, WWW, ACM WebScience), and published over one hundred papers; he was also recipient of three Yahoo! Faculty Awards and co-recipiend of a Google Focused Award, and member of many EU research projects. He was keynote speaker at many conferences such as ECIR, SPIRE, MFCS, IIR and invited scholar at the Institut des Hautes Études Scientifiques.
Ashok Goel
Professor, Georgia Institute of Technology, USADirector of Design & Intelligence Laboratory, Co-Director of Georgia Tech’s Center for Biologically Inspired Design
Bio - Ashok Goel is a Professor of Computer Science and Human-Centered Computing in the School of Interactive Computing at Georgia Institute of Technology. He is also the Chief Scientist with Georgia Tech's Center for 21st Century Universities. He conducts research into artificial intelligence and cognitive science with a focus on computational design and creativity. He is the Editor of AAAI’s AI Magazine and the Founding Editor of AAAI's Interactive AI Magazine (InteractiveAIMag.org).
Kristina Lerman
Research Professor of Computer Science and Principal Scientist at USC ISIBio - Kristina Lerman is a Principal Scientist at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Professor in the USC Computer Science Department. Trained as a physicist, she now applies network analysis and machine learning to problems in computational social science, including crowdsourcing, social networks and social media analysis. Her recent work on modeling and understanding cognitive biases in social networks has been covered by the Washington Post, Wall Street Journal, and MIT Tech Review.