April 2, 2003

Los Alamos lab names Cornell educator a distinguished scholar

ITHACA, N.Y. ---- Carlos Castillo-Chavez, professor of biomathematics and director of the Cornell University Mathematical and Theoretical Biology Institute (MTBI), has been named the 2003 Stanislaw M. Ulam Distinguished Scholar by the Center for Nonlinear Studies (CNLS) at Los Alamos National Laboratory.

Castillo-Chavez is spending this year at CNLS supervising seven MTBI alumni, most of them American Latino Ph.D.s and graduate students, in a program of diversified research. The research projects include influenza and dengue dynamics, homeland security and the study of epidemics on networks. Five of his collaborators are recipients of Cornell-Sloan fellowships in the mathematical and statistical sciences, a program that Castillo-Chavez founded in 1997 and now directs.

MTBI is a summer research program designed for undergraduates in the mathematical and biological sciences. Applications are encouraged from Latino, African-American, Native-American, and other minority students.

Castillo-Chavez's most recent accolade was the prestigious Distinguished Scientist Award by the Society for Advancement of Chicanos and Native Americans in Science in 2001. He is a native of Mexico who received his Ph.D. at the University of Wisconsin-Madison in 1984. He came to Cornell in 1985 as a postdoctoral student in the Department of Ecology and Evolutionary Biology. He joined the Cornell faculty as an assistant professor of biomathematics in 1988 and was promoted to full professor in 1997. He currently holds joint appointments in the departments of Statistics, Biological Statistics and Computational Biology, and Theoretical and Applied Mechanics.

The annual Ulam award honors the memory of the late Polish-American mathematician who was among the founders of what is now known as nonlinear science. He played a central role in the Manhattan Project, both during and after World War II. With physicist John von Neumann he developed the powerful statistical trial-and-error technique known as the Monte Carlo method.