People: Daniel Boley

Daniel Boley

Department of Computer Science and Engineering
4-192 EE/CSci Building
University of Minnesota
http://www-users.cs.umn.edu/~boley/
Ph: (612) 625-3887

Daniel Boley is a professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. He received a B.A. in Mathematics from Cornell University, an M.S. and Ph.D. in Computer Science from Standford University.

Boley’s current research interests include large sparse linear algebra problems arising from many engineering applications, the integration of numerical techniques in new technologies in a robust and fault tolerant manner, and the application of similar techniques to specific applications. Applications include control theory, electromagnetics, robotics, unsupervised machine learning, and vehicle navigation.

In machine learning, techniques related to spectral graph partitioning lead to unusually fast and effective methods for unsupervised clustering. This is an example of how fast linear algebra methods find their way into new areas. Using these techniques, we are exploring very large datasets derived from text documents, textile images, movie ratings, voice transcription data, etc. They also serve as a basis for a client-side Web agent capable of automatically organizing documents retrieved through the browser for the user.

In control theory, identification of systems from signal data using efficient state-space linear algebra techniques is an ongoing research effort. Our goal is to improve the robustness and numerical stability of the methods while not losing the advantages of a fast algorithm. Similar techniques carry over to the design and synthesis of controllers.

In electromagnetics and many similar applications in physics and engineering, the resonant modes lead naturally to very large sparse matrix eigenvalues, for which the efficient solution is sought. Iterative techniques lead to efficient solutions, but these depend on an appropriate ordering of the unknowns for sparsity, serial time complexity, and parallel efficiency.