Jeff Schneider
Associate Research Professor
Biography
Dr. Jeff Schneider is an associate research professor in the Carnegie
Mellon University School of Computer Science. He received his PhD in
Computer Science from the University of Rochester in 1995. He has over 15
years experience developing, publishing, and applying machine learning
algorithms in government, science, and industry. He has dozens of
publications and has given numerous invited talks and tutorials on the
subject.
Dr. Schneider was the co-founder and CEO of Schenley Park Research,
Inc. (SPR), a company dedicated to bringing new machine learning algorithms
to industry. Later, he developed a new machine-learning based CNS drug
discovery system and spent a two-year sabbatical as the Chief Informatics
Officer of Psychogenics, Inc. to set up and commercialize the system. Through
his work at CMU and his commercial and consulting efforts, he has worked
with several dozen companies and government agencies including ten Fortune
500 companies, and groups from around the world.
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 scientific, commercial, and government 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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Spectral Learning of Hidden Markov Models from Dynamic and Static Data
(2013)
-
A Composite Likelihood View for Multi-Label Classification
(2012)
A Composite Likelihood View for Multi-Label Classification -
An Impact Criterion for Active Graph Search
(2012)
soft-label model and impact criterion to solve active search -
Learning Bi-clustered Vector Autoregressive Models
(2012)
-
Maximum Margin Output Coding
(2012)
Maximum Margin Output Coding - (57 more)
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. -
Group Anomaly Detection Software
Matlab code of probabilistic models for group anomaly detection. -
Nonparametric Divergence Estimation
C++ and pure Matlab implementations of nonparametric divergence estimation. -
Source Code: Multi-Label Output Codes using Canonical Correlation Analysis
Multi-label Output Code using Canonical Correlation Analysis -
Active Model Fitting
Active Model Fitting -
Source Code: Maximum Margin Output Coding
Multi-label Output Code using Canonical Correlation Analysis