Thomas Dietterich - Challenges for Machine Learning in Ecological Science and Ecosystem Management

Thomas G. Dietterich (Oregon State University) presents as part of the UBC Department of Computer Science's Distinguished Lecture Series, October 20, 2011.

Just as machine learning has played a huge role in genomics, there are many problems in ecological science and ecosystem management that could be transformed by machine learning. This talk will give an overview of several research projects at Oregon State University in this area and discuss the novel machine learning problems that arise. These include (a) automated data cleaning and anomaly detection in sensor data streams, (b) automated classification of images of arthropod specimens, (c) species distribution modeling including modeling of bird migration from citizen science data, and (d) design of optimal policies for managing wildfires and invasive species. The machine learning challenges include flexible anomaly detection for multiple data streams, trainable high-precision object recognition systems, explicit models of sampling bias and measurement processes, combining probabilistic graphical models with non-parametric learning methods, and optimization of complex spatio-temporal Markov processes.

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