Statistical Analysis of Massive Data Streams: Proceedings of a Workshop (2004)
Chapter: Andrew Moore kd- R- Ball- and Ad- Trees: Scalable Massive Science Data Analysis
Andrew Moore
kd- R- Ball- and Ad- Trees: Scalable Massive Science Data Analysis
Transcript of Presentation and PDF Slides
BIOSKETCH: Andrew Moore is the A. Nico Habermann Professor of Robotics and Computer Science at the School of Computer Science, Carnegie Mellon University. His main research interest is data mining, using algorithms for finding all the potentially useful and statistically meaningful patterns in massive sources of data. He is most interested in learning graphical models efficiently, probabilistic models of person-person interactions, spatio-temporal algorithms for biosurveillance, active learning, new kinds of searches for interesting interactions between variables, and any kind of spatial data structure for caching sufficient statistics.
Dr. Moore began his career writing video games for an obscure British personal computer. He rapidly became a thousandaire and retired to academia, where he received a PhD from the University of Cambridge in 1991. He researched robot learning as a postdoc working with Chris Atkeson and then moved to a faculty position at Carnegie Mellon.
Dr. Moore’s research group, The Auton Lab, works with astrophysicists, biologists, marketing groups, bioinformaticists, manufacturers, and chemical engineers and is funded from industry and research grants from the National Science Foundation, NASA, and, more recently, the Defense Advanced Research Projects Agency. His research applications are in biosurveillance (he is part of a project led by Mike Wagner of the University of Pittsburgh) and intelligence analysis. Dr. Moore collaborates closely with Jeff Schneider and Chris Atkeson, who was his postdoctoral advisor at MIT.
