2019_05_Emma Mendel and Brad Cantrell
Emma Mendel and Brad Cantrell
Opening: September 20, 6:51 p.m.
Failure is at the heart of landscape: inherently complex environments of multi-valent relationships in constant flux with indeterminate beginnings and endings. As designers, the dominant paradigm avoids failure, attempting to predict externalities through ever more complex modes of simulation. Complexity beguiles prediction, making it impossible to predict whether or not one unprecedented event is more likely to occur over another. However, it is through the embrace of failure that we develop systems that more readily recover from disturbance, systems that lack fragility and have the ability to respond to unknown outcomes. This requires a paradigm shift: how do we operate in a world that understands it cannot predict a chaotic future, an environment that embraces complexity but rejects predictability?
Emma Mendel is a landscape designer who has worked for firms across North America. She is currently a lecturer at the University of Virginia’s Department of Landscape Architecture, researching and writing on topics pertaining to socio-cultural materiality, infrastructure and representation. She is an LAF Case Study Investigation Fellow and is a part of the Mellon Global South Humanities Lab; Mapping Indigenous Worlds at UVA. Her work has been published in OALA, the Princeton Architectural Journal, Kerb Landscape Architecture Journal, and is a winner of the LA+ Competition, Imagination.
Bradley Cantrell is chair and professor at the University of Virginia’s Department of Landscape Architecture and is fellow of the American Academy in Rome and a TED Global Fellow. His research focuses on computation to construct new types of landscape systems and ecologies. His work in Southern Louisiana looks closely at the relationship between large-scale delta infrastructure, real-time monitoring (sensing), and ecological fitness. He is the coauthor of several books, and has written numerous peer reviewed essays on the role of computation, machine learning, and robotics in landscape architecture.