ACM AAAI Allen Newell Award
University of New Mexico United States – 2011
CITATION

For fundamental, paradigm-changing contributions to computer science and biological sciences, most notably bringing together models of immune systems, automated diversity, and network epidemiology, with significant impact on real computer and biological systems research and practice.


Stephanie Forrest has contributed significantly to two areas: cyber security and biology. Her original contributions have introduced entirely new ways of looking at problems and solutions in these fields, in part by making explicit linkages between them. Starting in 1995, she began to write about how systems could develop a "sense of self" to detect anomalous and malicious behavior. The work expanded to eventually encompass a set of results in building "artificial immune systems" for computers and networks that led to commercial products and major government initiatives. Meanwhile, she was also conducting research into how biological immune systems work as compared to computational systems. That research resulted in new models of immunology, including new approaches to vaccine design and understanding viral replication.

Professor Forrest has research in artificial diversity, and non-linear complex systems. She was Vice President of Research and co-Chair of the Science Board at the Santa Fe Institute, and as a researcher with the Center for Nonlinear Studies at Los Alamos National Laboratory. She has also been active with the artificial life community. Her research in evolutionary software continues to expand in scope, and has also included work in automatic software fault correction, software (re)generation, and automated diversity for attack and flaw avoidance. Her work is truly interdisciplinary and has been recognized from both computer scientists and biologists. Her work in both of these areas has inspired many other researchers and advanced students. She has created new areas of inquiry that continue to intrigue scholars and produce new insight into real problems.