This NSF-funded projects seeks to develop algorithms for learning to classify items in sequential, spatial, and relational data. Application projects include sequence labeling problems in bioinformatics (protein secondary structure prediction, gene-finding, etc.), sequence labeling problems in language processing (part-of-speech tagging, shallow parsing, etc.), and pixel labeling problems in remote sensing (e.g., classifying pixels into land cover classes).
This NSF-managed project seeks to build a user interface that knows what tasks you are currently working on and can help you carry out those tasks. In particular, the system learns to predict your current task and then provide easy access to relevant documents, email addresses, webpages, and so on. We use the cluster to develop and test learning algorithms for this project.
This DARPA and NSF-funded project has as its goal to bridge the gap between knowledge representation and machine learning. Our goal is to develop a system in which you can describe a learning problem in a formal knowledge representation system and then the system automatically formulates a learning system to solve that problem. This involves the invention of features, selection among candidate features, and extraction and learning with those features. Our application areas include (a) modeling the spread of West Nile Virus and (b) predicting grasshopper infestations in Eastern Oregon, and (c) learning for the Task Tracer project.
In this NSF-funded project, we are developing image processing and learning algorithms for determining the genus and species of selected classes of insects from image data. We are also constructing a mechanical/optical device for manipulating and photographing insects.We are using the cluster to perform the image processing and to develop and test learning algorithms for this problem. Our two application tasks are (a) measuring stream health by recognizing stone fly larvae in stream substrate, and (b) measuring soil biodiversity by recognizing soil mesofauna in forest soils.
As the war against terrorism escalates, office buildings, transportation facilities, and sports arenas become tempting targets. We are developing models of pedestrian motion and the spaces they occupy. We have a microscopic crowd evacuation simulator that moves each individual pedestrian separately. Our goal is to develop a system capable of updating the positions of 100,000 or more people in real time.