 |
Shun-ichi Amari |
|
Samuel Kaski |
|
Tomaso Poggio |
| Professsor Shun-ichi Amari |
| Laboratory For Mathematical Neuroscience |
| Brain Science Institute |
| The Institute of Physical and Chemical Research (RIKEN) |
| Homepage |
| Email: amari@brain.riken.go.jp
|
| Abstract of talk |
Professor Shun-ichi Amari received his Ph.D. Degree in Mathematical Engineering in 1963 from University of Tokyo, Tokyo, Japan. Since 1981 he has held a professorship at the Department of Mathematical Engineering and Information Physics, University of Tokyo. He is a fellow of the IEEE and received the IEEE Neural Network Pioneer Award, the Japan Academy Award and the IEEE Emanuerl Piore Award. Professor Amari has served as a member of numerous editorial committee boards and organizing commitees and has published around 300 papers, including several book, in the areas of information theory and neural nets.
| Professor Samuel Kaski | ||||
| Laboratory of Computer and Information Science (Neural Networks Research Centre) | ||||
| Helsinki University of Technology | ||||
| | HomepageEmail: samuel.kaski@hut.fiAbstract of talk |
Samuel Kaski received the D.Sc. (PhD) degree in Computer Science from
Helsinki University of Technology, Espoo, Finland, in 1997. He is
currently Professor of Computer Science at the Laboratory of Computer
and Information Science (Neural Networks Research Centre), Helsinki
University of Technology, where he leads a research group on neural
computation-based data analysis. The current main applications are in
bioinformatics, finance, and natural language modeling.
| Professor Tomaso Poggio | ||||||
Department of Brain and Cognitive Sciences
|
Artificial Intelligence Laboratory
|
Massachusetts Institute of Technology
| | HomepageEmail: tp@ai.mit.edu
Abstract of talk |
Tomaso A. Poggio, Ph.D. is Uncas and Helen Whitaker Professor of Vision Sciences and Biophysics, Department of Brain and Cognitive Sciences; Co-Director, Center for Biological and Computational Learning, Artificial Intelligence Laboratory at MIT. His work is motivated by the belief that learning is at the core of any attempt at understanding the information processing problem involved in brain functions and the underlying neural mechanisms. Research on learning follows three basic directions: statistical theory of learning, engineering applications (in computer vision, computer graphics and artificial markets) and neuroscience of learning, presently focused on the problem of how the brain learns to recognize and represent objects.
|
Simon Haykin |
|
Sun-Yuan Kung |
| Professor Simon Haykin | ||||
Neurocomputation for Signal Processing Group
|
McMaster University
| | HomepageEmail: haykin@synapse.mcmaster.ca
|
Title of Talk: Beyond Stochastic Chaos: Implications for Dynamic Reconstruction
| Professor Sun-Yuan Kung | ||||
Dept. of Electrical Engineering
|
Princeton University
| | HomepageEmail: kung@ee.princeton.edu
|
Title of Talk: A Novel Associative Memory Approach to Blind SIMO/MIMO Channel Equalization and Signal Recovery
