Computational sustainability (CompSust) is an interdisciplinary research area that uses compu- tational methods to help address the 17 United Nations Sustainable Development Goals (UN SDGs), including but not limited to hunger and poverty reduction, infrastructure development, and environmental conservation. Computational sustainability is a two-way street: sustain- ability domains benefit from computational tools and methods and computational research areas benefit from the unique challenges that arise in attempting to address sustainability problems, including noisy and biased data, complex multi-agent systems, and multi-objective problems. Previous computational sustainability problems have led to new approaches in computer vision, reinforcement learning, multi-agent systems, and decision-focused learning. While computational sustainability problems span many domains, they share common challenges. This workshop will bring the community together to focus on two topics:1. The path from theory to deployment: Many challenges arise on the path from theory to deployment. This workshop will help researchers navigate this path by bringing together participants and speakers from academia, industry, and non-profits, highlighting successes going from theory to deployment, and facilitating collaboration.2. Promises and pitfalls: Advances on ML benchmarks do not always translate to improvements in computational sustainability problems, with contributing factors including low- signal-to-noise ratios, ever changing conditions, and biased or imbalanced data. However, due to the difficulties of publishing negative results, these findings rarely reach the community leading to duplicated effort and obscuring important gaps in existing methods.The goals of this workshop are to (i) identify pathways from theory to deployment, including best-practices and measures to quantify success, (ii) facilitate discussion and collaboration between participants from academia, industry, and the non-profit sector, and (iii) identify common failure modes and high-impact research directions, including “moonshot” challenges.
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