Machine Learning for Environmental Sustainability
The advances in machine learning are revolutionizing how we understand and design effective strategies to deal with forest and other natural resource sustainability challenges. In this talk, I will show how state-of-the art machine learning algorithms advance analysis of the social and ecological impacts of natural resource policies and generate evidence to prioritize governance actions to further environmental sustainability goals. I will also explore reasons for slow adoption of automated decisionmaking in natural resource management and conclude with possible directions for mainstreaming artificial intelligence and machine learning in forest conservation and management.
I am an interdisciplinary environmental social scientist interested in evaluating and predicting the impacts of forest conservation policies at local and global scales to build knowledge on sustainable Social-Ecological Systems (SESs). I am particularly interested in exploring use of artificial intelligence and machine learning approaches in environmental sciences especially in the fields of policy impact evaluation, forest conservation and management. I have also experience in using advanced spatial analytical techniques, machine learning and causal inference based statistical approaches to explore research objectives in the field of environmental and forest governance.