2007.08.28 | Lars Allan Arge

This talk discusses combinatorial group testing, which began from work on detecting diseases in blood samples taken from GIs in WWII. Given a parameter d, which provides an upper bound on the number of defective (e.g., diseased) samples, the main objective of such problems is todesign algorithms that identify all the defective samples without explicitly testing all n samples. This classic problem has a number of interesting modern applications, and we provide several new efficient algorithms that can be applied in these new contexts. In particular, modern applications we will discuss include problems in DNA sequencing, wireless broadcasting, and network security.

Biography:
Prof. Goodrich received his B.A. in Mathematics and Computer Science from Calvin College in 1983 and his PhD in Computer Sciences from Purdue University in 1987. He is a Chancellor's Professor at the University of California, Irvine, where he has been a faculty member in the Department of Computer Science since 2001. Dr. Goodrich's research is directed at the design of high performance algorithms and data structures for solving large-scale problems motivated from information assurance and security, the Internet, information visualization, and geometric computing.