TITLE: Concern for Speed, Accuracy, Energy Awareness, and Space-time Complexity in Advanced AI Algorithms

Professor Dr. Chidchanok Lursinsap
Department of Mathematics and Computer Science
Faculty of Science
Chulalongkorn University
Bangkok 10330, Thailand

Abstract:

The competitive success in applying machine intelligence to business, advancement in science finding, new engineering innovation as well as medical treatment requires fast computation and spends less resources with low energy consumption. Current algorithms do not holistically concern the issues of (1) speed, (2) accuracy, (3) energy awareness (tremendous number of processors are used), and (4) space-time complexities. In fact, these four issues must be involved while developing the algorithms. In this talk, two main advanced artificial intelligence algorithms which are meta-heuristic algorithm and new neural learning with elastic structural network will be discussed in detail with some examples. Furthermore, the capability to cope the scenario of dynamically abrupt class changes and imbalanced classes in the challenging big streaming data environment of memory overflow and preserve of classical space-time complexities of computation will also be focused.

Biography

CHIDCHANOK LURSINSAP received the B.Eng.degree (Hons.) in computer engineering from Chulalongkorn University, Bangkok, Thailand, in 1978, and the M.S. and Ph.D. degrees in computer science from the University of Illinois at Urbana–Champaign, Urbana, IL, USA, in 1982 and 1986, respectively. He was a Lecturer with the Department of Computer Engineering, Chulalongkorn University, in 1979. In 1986, he was a Visiting Assistant Professor with the Department of Computer Science, University of Illinois at Urbana–Champaign. From 1987 to 1996, he was with the Center for Advanced Computer Studies, University of Louisiana at Lafayette, as an Assistant and an Associate Professor. After that, he came back to Thailand to establish the Ph.D.  Program in computer science with Chulalongkorn University and he became a Full Professor. His major research interests include neural learning and its applications to other science and engineering areas.