Balancing environmental, economic, and societal needs for a sustainable future encompasses problems of unprecedented size and complexity. With naturally occuring settings, global scale, dynamic and uncertain behavior, mixture of discrete and continuous effects, and highly interactive components, problems associated with sustaining the earth's resources can greatly benefit from computational methods and thinking. There is a key role to be played by computing and information sciences in increasing the efficiency and effectiveness in the way humanity manages and allocates natural resources. Toward that objective, this Expedition aims to establish and nurture a new field of study--Computational Sustainability--driven by a wide range of hard computational problems and critical challenges in the area of sustainability. This applied theoretical Expedition will pursue interdisciplinary research across three computational sustainability themes: conservation and biodiversity; balancing socio-economic demands and the environment; and renewable energy. With the view that natural problems may have a special structure discoverable by machine learning techniques that allows them to be solved even though they are NP-hard, this research attempts to stimulating new research synergies that cross boundaries and merge ideas from combinatorial optimization, dynamical systems, machine learning and constraint reasoning. An "Institute for Computational Sustainability" will be based at Cornell to serve as the nexus of foundational science advancements and practical applications in sustainability. Part of its mission and outreach is to establish a vibrant and diverse research community in the area of computational sustainability, drawing new students into the field from all backgrounds including students from underrepresented groups via summer research experiences and other such proactive activities.
