ODSC Speakers 48/72

ODSC Speakers 48/72



Abstract: 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 predicted economic well-being using only high-resolution satellite imagery. Due images are passively collected in every corner of the world, our method can Prepared and accurate measurements in a very scalable end economic way, and could revolutionize progress toward global poverty eradication. As a second example, I will present some ongoing work on monitoring food security outcomes.

Bio : I am an assistant professor in the Department of Computer Science at Stanford University, where I am affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment.

My research is centered on techniques for scalability and accurate inference 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