About Me
I am a Ph.D. candidate in the Machine
Learning Department at Carnegie Mellon University,
working with Geoff Gordon.
Prior to joining the doctoral program, I did a M.Sc.
in Computing Science at the University of Alberta
with Russell Greiner.
My research interests center on probabilistic models
for relational and link structured data.
Such data often appears as graphs or relational databases,
where classical independence assumptions do not hold.
I have also worked on query variance
estimation and structure learning in graphical models,
cascades in graphs, and active learning in sensor networks.
Recent Publications
A. Krause, A. Singh & C. Guestrin. Near-Optimal Sensor Placements in Gaussian Process: Theory, Efficient Algorithms and Empirical Studies. J. Machine Learning Research. vol. 9, 2008. [Details]
T. van Allen, A. Singh, R. Greiner, & P. Hooper. Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference. Artificial Intelligence. vol 172(4-5), 2008. [Details]