Shannon Lecture Series: Jan. 29, 2004.
Developments in Shannon Sampling Theory
Presented by: Stephen Smale

Abstract of Talk.

Shannon sampling is a special case of the general problem of reconstructing a function from its values at a discrete set of points. This is often the essential underlying problem for machine learning, data mining, etc.

The talk will deal with age-old algorithms for solving this problem and new estimates of their accuracy and efficiency.

About the Speaker.

Stephen Smale is Professor Emeritus at U. C. Berkeley and Professor. Since 1995 he has been at the City University of Hong Kong and more recently at Toyota Technical Institute at Chicago, University of Chicago. Professor Smale was awarded the Fields Medal at the 1966 International Congress of Mathematicians for his work on the generalized Poincaré conjecture. Since the late 1960's he has been very active in applied areas utilizing dynamical systems methods and creating new methods in various areas including electrical circuit theory. He has made significant advances in the theory of convergence of algorithms and areas of complexity.

Return to Santa Clara Valley Chapter IEEE Computer Society page.