Biography: Dr. Hideyasu "Hide" Sasaki is a Senior Researcher in the ICT Testbed Research and Development Promotion Center at National Institute of Information and Communications Technology (NICT), Tokyo, Japan. He received his Ph.D. in Computer Science from Keio University, Tokyo, Japan, in 2003. He joined the faculty of Keio University in 2003, as an Assistant Professor. In 2005, he joined Ritsumeikan University, Kyoto, Japan, as an Associate Professor with tenure. In 2015, he moved back to Keio University as a Senior Researcher. Since April 2016, he has been with National Institute of Information and Communications Technology (NICT), Tokyo, Japan, where he is currently tenured as a Senior Researcher for Computational Intelligence Research. In 2011 and 2012, he was an Honorary Research Associate with the Artificial Intelligence Laboratory, the Department of Computer Science and Engineering, The Chinese University of Hong Kong. In 2012, he was a Visiting Professor with the Department of Electronic Engineering and Computer Science, VŠB Technological University of Ostrava, Moravian-Silesian, Czech Republic, a Visiting Professor with the School of Design Engineering, Brunel University, Uxbridge, U.K., an Academic Visitor with the Computational Intelligence Research Group, the Department of Computer Science, The University of Oxford, U.K., a Visiting Researcher with the Computational Intelligence Laboratory, the School of Computer Science, The University of Birmingham, U.K., a Visiting Researcher with the School of Computing, The University of Salford, U.K., a Visiting Researcher with the Faculty of Science and Technology, The University of Westminster, London, U.K., and a Visiting Researcher with the School of Computing, The University of Portsmouth, U.K.. He has published more than 50 articles in journals and conference proceedings covering areas of Computational Intelligence and Decision Making. His current research interests include bio-inspired computing, biological big data modeling and analytics, decision making under time constraint, machine learning and computational algorithms.
From 2012 to 2015, Dr. Sasaki served as the Chair of the Task Force on Bio-Inspired Self-Organizing Collective Systems and as a Formal Member at the Emergent Technologies Technical Committee (ETTC) of the IEEE COMPUTATIONAL INTELLIGENCE SOCIETY (CIS). In 2011, he served as a Reviewer for the Research Councils of United Kingdom (RCUK). In 2008, he founded the International Journal of Organizational and Collective Intelligence (IGI Global Press, N.J., U.S.A.) and served as the Editor-in-Chief until 2013. He is (or has been) an Associate Editor for five international journals, and a Chair, Session Chair, or Technical/Program Committee Member for a number of international conferences on computational intelligence and soft computing since 2002.
Dr. Sasaki has presented a keynote speech at WITC 2011, ServComp 2011, and CICN 2010, and given invited talks at four international conferences, including the SPIE Defense and Security Symposium sponsored by DARPA in 2011. He was awarded with The Fourth Annual Excellence in Research Journal Award (ISSSOE, IGI Global Press) in 2010, IARIA Fellow in 2010, twice Best Paper Award (ICIW) in 2009 and 2008, Microsoft Intellectual Property Research Award in 2005, and Japan’s National Police Academy Best Paper Award in 1999. His research has been supported by Japan’s NSF (JSPS).
Before his active work in the area of computer science, Dr. Sasaki graduated from the University of Chicago Law School in 1999, and practiced law as an Attorney, in N.Y., U.S.A..
Speech Title: Collective Intelligence Modeling in Time Sensitive Decision Making
Abstract: Collective intelligence is a nature-inspired problem solving technique that has been highly appreciated in machine learning and data mining of Big Data. Time-sensitive decision making highlights the next challenge of Big Data learning by deep neural networks. In this talk, after short briefing where collective intelligence survives at the advent of deep learning modeling, I will present two collective intelligence models for time-sensitive decision making. The first model is on bilateral decision making, and is formulated by introducing collective intelligence about human-machine interaction that dramatically accelerates the decision-making speed. Moreover, that model is enhanced into multilateral decision making under time constraint.
The second model applies collective intelligence, which is found in modeling human-machine interaction under time constraint, to very big biological data. In analytics of behaviors of the biological Big Data, we discovered how tiny creatures like ants make a wise choice in moving between an old and a new nest within a very short period of time. The ant’s moving is modeled and reduced into a simple probabilistic distribution that shows a very powerful way collective intelligence works in time-sensitive decision making. Through discussing the two collective intelligence models, we would offer exemplary cases on where collective intelligence, furthermore heuristics, that machine learning and data mining have discovered until the present time, contributes to the future research of time-sensitive deep learning.