Keynote Speeches

1. Parallel Problem Solving in Honeybees: Vision, Navigation and `Cognition'

Mandyam V. Srinivasan
Centre for Visual Sciences, Research School of Biological Sciences,
Australian National University


Anyone who has watched a fly make a flawless landing on the rim of a teacup, or marvelled at a honeybee speeding home after collecting nectar from a flower patch several kilometres away, would know that insects possess visual systems that are fast, reliable and accurate. Insects cope remarkably well with their world, despite possessing a brain that carries fewer than 0.01% as many neurons as ours does. This talk will explore the secrets of their success, by describing research aimed at understanding the mechanisms underlying visual perception, navigation, learning, memory and 'cognition' in honeybees. The application of insect-based principles to the design of novel, autonomous land-based and aerial vehicles will also be described.

Speaker's Bio Sketch: Mandyam Srinivasan is currently Professor of Visual Science at the Australian National University's Research School of Biological Sciences, and Director of the University's Centre for Visual Science.

He received a B.E. in Electrical Engineering from Bangalore University, an M.E. in Electrical Engineering from the Indian Institute of Science, a Ph.D. in Engineering and Applied Science from Yale University, and a D.Sc. in Neuroethology from the Australian National University. He is a Fellow of the Australian Academy of Science, a Fellow of the Royal Society of London, and an Inaugural ARC Federation Fellow. 

Srinivasan's research is directed at understanding principles of vision, navigation and "cognition' in small animals, and in applying some of these ideas to the design of biologically inspired algorithms for machine vision and robotics.

2. Constraint Partitioning and its Applications In Parallel Problem Solving

Benjamin W. Wah
Department of Electrical and Computer Engineering
and the Coordinated Science Laboratory
University of Illinois, Urbana-Champaign
Urbana, IL 61801

The solution of mixed-integer constrained optimization problems with highly nonlinear objectives and/or constraints has long been considered challenging. These problems are abundant in many engineering applications, including IC design automation, signal processing, temporal planning, scheduling, computer aided manufacturing, robotics, and neural-network learning. In this talk, we present a new approach that partitions a nonlinear optimization problem by their constraints into subproblems, that evaluates each subproblem independently, and that resolves inconsistent global constraints across subproblems. The evaluation of each subproblem and the resolution of inconsistent global constraints are based on a formal mathematical foundation of extended saddle points that is proved to be necessary and sufficient for characterizing constrained local minima in these problems. The advantage of our partitioning approach is that it allows complex problems to be solved as a number of simpler subproblems, each with significantly fewer constraints, before resolving violated global constraints that relate the subproblems. We discuss our approach in parallel problem solving by illustrating applications in temporal planning, neural-network learning, signal processing, and nonlinear optimization.

Biography. Benjamin W. Wah is currently the Franklin W. Woeltge Endowed Professor of Electrical and Computer Engineering and Professor of the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign, Urbana, IL. He received his Ph.D. degree in computer science from the University of California, Berkeley, CA, in 1979. Previously, he had served on the faculty of Purdue University (1979-85), as a Program Director at the National Science Foundation (1988-89), as Fujitsu Visiting Chair Professor of Intelligence Engineering, University of Tokyo (1992), and McKay Visiting Professor of Electrical Engineering and Computer Science, University of California, Berkeley (1994). In 1989, he was awarded a University Scholar of the University of Illinois; in 1998, he received the IEEE Computer Society Technical Achievement Award; in 2000, the IEEE Millennium Medal; and in 2003, the Raymond T. Yeh Lifetime Achievement Award from the Society for Design and Process Science. Wah's current research interests are in the areas of nonlinear search and optimization, multimedia signal processing, and computer networks.

Wah was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering between 1993 and 1996, and is the Honorary Editor-in-Chief of Knowledge and Information Systems. He currently serves on the editorial boards of Information Sciences, International Journal on Artificial Intelligence Tools, Journal of VLSI Signal Processing, World Wide Web, and Neural Processing Letters. He had chaired a number of international conferences and was the International Program Committee Chair of the IFIP World Congress in 2000. He has served the IEEE Computer Society in various capacities, including Vice President for Publications (1998 and 1999) and President (2001). He is a Fellow of the IEEE.

3. Next Generation CiteSeer

Dr. C. Lee Giles
David Reese Professor, School of Information Sciences and Technology
Professor, Computer Science and Engineering
Professor, Supply Chain and Information Systems
The Pennsylvania State University
University Park, PA

CiteSeer, a computer and information science search engine and digital library, has been a radical departure for scientific document access and analysis. With nearly 700,000 documents, it has sometimes two million page views a day making it one of the most popular document access engines in science. CiteSeer is also portable, having been extended to ebusiness (eBizSearch) and more recently to academic business documents (SMEALSearch). CiteSeer is based on two features: actively acquiring new documents and automatic tagging and linking of metadata information inherent in an academic documentís syntactic structure. Why is CiteSeer so popular? We discuss this and methods for providing new tagged metadata such as institutions and acknowledgements, new data resources and services and the issues in automation. We then discuss the next generation of CiteSeer.

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