Jeff Schneider

schneide@cs.cmu.edu

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

Software

• 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.