V. Rao Vemuri

Univeristy of California at Davis
and Lawrence Livermore National Laboratory
P.O. BOX 808, L-794
Livermore, CA 94550
Phone: (510)424-4051
Fax:   (510)422-8681
Email: vemuri1@llnl.gov

Biographical Information

Prof. V. Rao Vemuri is with the Department of Applied Science, the Graduate Group in Computer Science, and the Graduate Group in Biomedical Engineering. He has 18 years of academic experience and 9 years of industrial experience. He is the author of six books and over sixty journal publications. His research areas include modeling, simulation, numerical methods, neural networks, genetic algorithms and their applications to signal processing, digital communications, optimization, control systems and user interface design. He is a member of ACM, senior member of IEEE and a former Editor-in-Chief of CS Press.

Suggested Lecture Topics

Artificial Neural Networks: What can they do?

Artificial neural nets are being accepted as alternatives to the traditional computational paradigms. Although neural nets were first used to solve computationally hard problems like cognition and recognition, now they are being used to solve a wide variety of mathematical tasks such as curve fitti ng, matrix inversion, time series forecasting and so on. This introductory talk will walk you through a variety of examples to illustrate the power and versatility of artificial neural nets.

Genetic Algorithms and Genetic Programming

A genetic algorithm is a specialized kind of search procedure that use s a number of biological metaphors in the process of optimization. This introduct ory talk presents the highlights of the method using a simple example as a vehicle. Discussion then turns toward genetic programming, a paradigm whose goal is to automatically generate correct computer programs from a population randomly selected program segments.

Advanced Topics in Genetic Algorithms and Neural Nets

This talk will cover, in greater depth, one of the topics listed below. Use of genetic algorithms in (a) multi modal optimization, (b) DNA fragment a ssembly problem. Use of artificial neural nets in seismic signal processing. Each of these three talks go into greater depth and may be of interest to specialized groups. The chapter is encouraged to discuss their level of interest.


Association for Computing Machinery Technology Outreach Program
Last modified: Aug 3, 2004