About the series
Bio: Stephanie Forrest is a Professor of Computer Science at Arizona State University, where she directs the Biodesign Center for Biocomputation, Security, and Society. Her research focuses on the intersection of biology and computation, including cybersecurity, software engineering, and biological modeling.
Prior to joining ASU in 2017, she was at the University of New Mexico and served as Dept. Chair 2006-2011. She is a member of the Santa Fe Institute External Faculty and 2013-2014 served at the U.S. Dept. of State as a Senior Science Advisor for cyberpolicy. She was educated at St. John's College (B.A.) and the University of Michigan (M.S. and Ph.D. in Computer Science).
Some of her awards include: The 2023 IEEE Computational Intelligence Pioneer Award; The 2020 Test of Time Award from the IEEE Security and Privacy Symposium; The 2019 Most Influential Paper Award from the International Conference on Software Engineering, the ACM/AAAI Allen Newell Award (2011), and the NSF Presidential Young Investigator Award (1991). She is a Fellow of the IEEE and a member of the Computing Research Association Board of Directors, where she Chairs its Government Affairs Committee.
Talk Abstract: Software today is an evolving adaptive system. Although we think of computer programs as the products of intelligent design, they also evolve inadvertently through the actions of many individual programmers, often leading to unanticipated consequences. Similarly, economic and political incentives produce arms races between competitors and adversaries, which in turn have shaped the cyber landscape. Because software is subject to constraints similar to those faced by evolving biological systems, we have much to gain by viewing computing through the lens of biology. The talk will highlight research applying the mechanisms of evolution quite directly to software, including repairing bugs, closing vulnerabilities, and optimizing GPU codes. The results have implications for how we think more generally about engineering complex systems that are subject to evolutionary pressures and engineering constraints.
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