Wednesday, November 6 NOON King 237
Thomas G. Dietterich of Oregon State University will present:
Opportunities for Machine Learning in Ecological Science and Ecosystem Management How can computer science address the many challenges of managing the earth's ecosystems sustainably? Viewed as a control problem, ecosystem management is challenging for two reasons. First, we lack good models of the function and structure of the earth's ecosystems. Second, it is difficult to compute optimal management policies because ecosystems exhibit complex spatio-temporal interactions at multiple scales. This talk will discuss some of the many challenges and opportunities for machine learning research in computational sustainability. These include sensor placement, data interpretation, model fitting, computing robust optimal policies, and finally executing those policies successfully. I'll provide examples from current work and discuss open problems in each of these areas. All of these sustainability problems involve spatial modeling and optimization, and all of them can be usefully conceived in terms of facilitating or preventing flows along edges in spatial networks. For example, encouraging the recovery of endangered species involves creating a network of suitable habitat and encouraging spread along the edges of the network. Conversely, preventing the spread of diseases, invasive species, and pollutants involves preventing flow along edges of networks. Addressing these problems will require advances in several areas of machine learning and optimization.