Jeff Schneider
Associate Research Professor
Biography
Dr. Jeff Schneider is currently a research scientist in the Carnegie Mellon University School of Computer Science. He received his PhD in Computer Science from the University of Rochester in 1995, and his BS in Computer Science from Michigan State University in 1988. Jeff's research interests include machine learning, data mining, active learning, optimization, learning control. He is especially interested in the commercial applications of these methods and has pursued this through a startup company he co-founded with Andrew Moore, Schenley Park Research. Through the company and his university research Jeff has worked with a couple dozen industrial companies and government agencies including six Fortune 500 companies, and groups from four other countries. Jeff has published extensively in the machine learning community and given numerous talks and tutorials on the subject.
Research Interests
My research is in learning, optimization, data mining, scheduling, robotics, and intelligent control. I am also interested in related areas including navigation, manipulation, vision, and other types of sensing. My efforts are focused on the application of these methods to real-world industrial and commercial problems.
Tags
Active Learning, Applications, Astrostatistics, Bayesian Networks, Biosurveillance, Cached Sufficient Statistics, Efficient Statistical Algorithms, GDA, Kd-trees and Ball-trees, Link Analysis, Locally Weighted Learning, Markov Decision Processes, Memory-based Learning, Optimization, Spatial Statistics, Statistical Data Mining for Astrophysics, WSARE
Recent Papers
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Actively Learning Level-Sets of Composite Functions
(2008)
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A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data
(2007)
NSF Biosurveillance Workshop 2007 -
Detecting Anomalous Records in Categorical Datasets
(2007)
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Mapping the Cosmological Confidence Ball Surface
(2007)
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Efficiently Computing Minimax Expected-Size Confidence Regions
(2007)
- (29 more)
Software
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cGraph
CGraph is an algorithm to quickly learn a graph-based model of the underlying connections of a set of entities given link data. -
k-groups/GDA
The group detection algorithm (GDA) finds underlying groupings of entities given a set of observed links and demographic information. -
Vizier
Old but fast locally weighted regression for Windows.