Opportunities for Machine Learning in Ecological Science and Ecosystem Management

October 30th, 2013

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.