Dear Colleagues,
We are pleased to kick-off the Computational Sustainability Virtual Seminar Series with a talk by Professor Stefano Ermon of Stanford University, co-author on ”Combining satellite imagery and machine learning to predict poverty” (Science, August 19, 2016) .
Anyone may register here to receive connection details on this Zoom webinar (it’s free!). Please distribute this link to others who may be interested, especially colleagues and students.
The Computational Sustainability Virtual Seminar Series first talk is scheduled for
Tuesday, September 27, 2016
4:00 – 5:00 pm Eastern Time (1:00 – 2:00 pm Pacific Time)
Measuring progress towards sustainable development goals with machine learning
Stefano Ermon, Stanford University
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 agricultural and food security outcomes from space.
BIO: Stefano Ermon is currently an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and the Woods Institute for the Environment. He completed his PhD in computer science at Cornell in 2015. His research interests include techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. Stefano has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a McMullen Fellowship.
The Computational Sustainability Virtual Seminar Series will present talks by researchers and educators in Computational Sustainability, and is being sponsored by CompSustNet, with support from the National Science Foundation’s Expeditions in Computing program.
Best regards,
Carla Gomes and Doug Fisher
Watch this video to learn more about the Ermon Lab’s “Combining Satellite Imagery and Machine Learning to predict poverty”
Event listing on the CompSustNet web site