"It’s great, especially since I’m a new faculty member. It’s great also because of the research benefit—how my research is going to shape [computer science] over the next 10 years, " said Zaki.

In winning the award, Zaki received a five-year, $300,000 grant for his research work on the scalable, parallel, and interactive data mining and exploration at Rensselaer project.

The SPIDER project has 32 Pentium processors running in parallel on a high-speed network.

The project’s goal is to develop data mining techniques that are both "customized to a domain" and can be used generically in a wide array of plug and play systems, said Zaki.

Data mining search techniques are developed to facilitate the analysis of significant correlations between information stored in databases such as structural property relationships of materials, the three-dimensional shape of proteins, and the effect of high pressure and other factors on aircraft wings.

According to Zaki, data mining has three primary layers, or components.

The first, applying specifications to input, involves the analysis of the system’s domain and expressing characteristics in abstract terms called modular primitives.

In the second, researchers develop data mining tasks built on performance operations.

High performance clusters in the third and final stage put data mining tasks into operation in order to test their effectiveness.

Zaki received his M.S. and Ph.D. degrees in computer science from the University of Rochester in 1995 and 1998. Prior to that, he received his B.S. in computer science and mathematics from Angelo State University in 1993.

Zaki has been a professor at RPI since the fall semester 1998.