Machine Learning for Sustainability

Sustainability problems pose one of the greatest challenges facing society. Humans consume more than 16TW of power, about 84% of which comes from unsustainable fossil fuels. In addition to simply being a finite resource, the carbon released from fossil fuels is a significant driver of climate change and could have a profound impact on our environment. In addition to carbon releases, humans are modifying the ecosphere in many ways that are leading to large changes in the function and structure of ecosystems. These include huge releases of nitrogen from fertilizers, the collapse and extinction of many species, and the unsustainable harvest of natural resources (e.g., fish, timber). While sustainability problems span many disciplines, several tasks in this space are fundamentally prediction, modeling, and control tasks, areas where machine learning can have a large impact. Many of these problems also require the development of novel machine learning methods, particularly methods that can scale to very large spatio-temporal problem instances.

In recent years there has been growing interest in applying machine to problems of sustainability, spanning applications in energy, environmental management, and climate modeling. The goal of this workshop will be to bring together researchers from both the machine learning and sustainability application fields to continue and build upon this emerging area. The talks and posters will span general discussions of sustainability issues, specific sustainability-related data sets and problem domains, and ongoing work on developing and applying machine learning techniques to these tasks.