M112 Alway Building, Medical Center
(next to the Dean's courtyard)
|DATE:||May 11, 2017|
|TIME:||1:30 - 2:50 pm|
|TITLE:||Artificial Intelligence for Sustainability|
Assistant Professor of Computer Science and Center Fellow, by courtesy,
at the Woods Institute for the Environment, Stanford
Recent technological developments are creating new spatio-temporal data streams that contain a wealth of information relevant to sustainable development goals. Modern AI techniques have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy. As a first example, I will present a machine learning method we developed to predict and map poverty in developing countries. Our method can reliably predict economic well-being using only high-resolution satellite imagery. Because images are passively collected in every corner of the world, our method can provide timely and accurate measurements in a very scalable end economic way, and could revolutionize efforts towards global poverty eradication. As a second example, I will present some ongoing work on monitoring food security outcomes.
Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon. Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.
Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David Lobell, Stefano Ermon. Combining Satellite Imagery and Machine Learning to Predict Poverty. Science. In Science, 353(6301), 790-794, 2016.
Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing. Deep Kernel Learning. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.