Dougal J. Sutherland
I'm a fifth-year Ph.D. student in the CS department
at Carnegie Mellon University,
working with Jeff Schneider on machine learning.
My work is supported by a Sandia Campus Executive Program fellowship — thanks, Sandia!
I'm primarily interested in learning on sets and distributions
and in active learning, particularly in nonstandard settings such as matrices and searching for large-scale patterns.
My thesis proposal (and proposal slides) has more details.
My personal page has non-research stuff.
Some research code is on github;
some of that is signed with my GPG key.
I also pretend that crossvalidated and stackoverflow are kind of like research.
denotes equal contribution.
Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning.
Journal and Peer-Reviewed Conference Papers
Linear-time Learning on Distributions with Approximate Kernel Embeddings.
On the Error of Random Fourier Features.
A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters.
The Astrophysical Journal (ApJ), 803, 50 (2015).
Active learning and search on low-rank matrices.
Nonparametric kernel estimators for image classification.
Managing User Requests with the Grand Unified Task System (GUTS).
Kernels on Sample Sets via Nonparametric Divergence Estimates.
Grounding Conceptual Knowledge with Spatio-Temporal Multi-Dimensional Relational Framework Trees.
University of Oklahoma Artificial Intelligence and Robotics Technical Report #1138 (2012).