Challenges in Computational Sustainability

Bistra Dilkina Dept. of Computer Science Cornell University

Abstract:

Computational sustainability is a new interdisciplinary research focused on computational problems that arise in the quest for sustainable development. The goal of sustainable development, a notion introduced in 1987 by the seminal report of the United Nations World Commission on Environment and Development, is to balance environmental, economic, and societal factors to "meet the needs of the present without compromising the ability of future generations to meet their own needs." In this talk, I will provide a sample of computational sustainability problems, from the areas of wildlife conservation, biodiversity, poverty mitigation, climate and environment monitoring. I will describe, for example, network design problems motivated by challenging planning problems in wildlife conservation. In this context, I will present a network design optimization framework for stochastic diffusion processes, such as species dispersal, fire spread, information propagation, and disease outbreak. I will also emphasize the unique opportunities for computing and information scientists to contribute to the new research area of computational sustainability and show how the challenges in this area call for an integration of work from different areas in computer science, such as constraint reasoning, optimization, algorithm design, machine learning, citizen science, and human computation.

Bio: Bistra Dilkina is a postdoctoral researcher at the Institute for Computational Sustainability. She received a Ph.D. in computer science from Cornell University in 2012. Her research interests lie at the intersection of artificial intelligence and operations research, and machine learning, with a focus on advancing the state of the art in combinatorial optimization techniques for solving real-world large-scale problems, particularly ones that arise in sustainability areas such as conservation planning. Her work spans constraint reasoning and optimization, network design, stochastic optimization, machine learning, and game theory.