Talkative Sensors: Collaborative Machine Learning for Volcano Sensor Networks
When: Monday, September 26, 2011 (4:00-4:50pm) Where: KEC 1001 Speaker Name: Kiri Wagstaff Speaker Title/Description: Senior Researcher, NASA Jet Propulsion LabSpeaker Biography: Kiri Wagstaff is a senior researcher in artificial intelligence and machine learning at the Jet Propulsion Laboratory, on sabbatical at Oregon State University for the fall term. Her focus is on developing new machine learning and data analysis methods, particularly those that can be used for in situ analysis onboard spacecraft (orbiters, landers, etc.). She has developed several classifiers and detectors for data collected by instruments on the EO-1 Earth orbiter, Mars Pathfinder, and Mars Odyssey. The applications range from detecting dust storms on Mars to predicting crop yield on Earth. She holds a Ph.D. in Computer Science from Cornell University (2002) and an M.S. in Geological Sciences from the University of Southern California (2008).
Imagine a machine learning agent deployed at each station in a sensor network, so that it can analyze incoming data and determine when something interesting happens. Traditionally, this analysis would be done independently at each station. But what if each agent could talk to its neighbors and find out what they're seeing? We've developed a learning system that enables collaboration so that the agents can autonomously (without human input) improve their performance. Each agent can ask its neighbors for their opinions, then use them to refine its own results. When each agent is given the task of clustering the observed data, the opinions are expressed in the form of pairwise clustering constraints. We evaluated several heuristics for selecting which items an agent should query and found that the best strategy was to select one item close to its assigned cluster and one item at the boundary between two clusters. We applied this technique to seismic and infrasonic data collected by the Mount Erebus Volcano Observatory, in which the goal was to separate eruptions from non-eruptions. Collaborative clustering achieved a 150% improvement over regular, non-collaborative clustering. This is joint work with Jillian Green (California State Univ., Los Angeles), Rich Aster and Hunter York (New Mexico Institute of Mining and Technology), Terran Lane (Univ. of NM), and Umaa Rebbapragada (JPL), funded by the NSF.
_______________________________________________ Colloquium mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/colloquium
