Center for Computing Research (CCR)

Center for Computing Research

Daniel Dunlavy

Daniel Dunlavy
Scalable Analysis and Vis
Email: dmdunla@sandia.gov
Phone: 505/284-6092
Fax: 505/844-4728

Mailing address:
Sandia National Laboratories
P.O. Box 5800, MS 1327
Albuquerque, NM
87185-1320

Daniel (Danny) Dunlavy is a Principal R&D Staff Member in the Scalable Analysis and Visualization Department at Sandia National Laboratories in Albuquerque, NM. His research interests include numerical optimization, numerical linear algebra, machine learning, data mining, tensor decompositions, hypergraph algorithms, text analysis, parallel computing, and cyber security. 

Education/Background

  • Ph.D., Applied Mathematics and Scientific Computation, University of Maryland, College Park, 2005
  • M.S., Applied Mathematics and Scientific Computation, University of Maryland, College Park, 2003
  • M.S., Applied Mathematics, Western Michigan University, 2001
  • B.A., Computer Studies, Northwestern University, 1994

Expanded Personal Web Page

Software

Selected Publications & Presentations

2016
2013
2011
  • Acar, Evrim, Tamara G. Kolda, Daniel M. Dunlavy, "All-at-once Optimization for Coupled Matrix and Tensor Factorizations," Conference Paper, MLG'11: Mining and Learning with Graphs, August 2011.
  • Acar, Evrim, Daniel M. Dunlavy, Tamara G. Kolda, "A Scalable Optimization Approach for Fitting Canonical Tensor Decompositions," Journal Article, Journal of Chemometrics, Vol. 25, No. 2, pp. 67–86, Accepted/Published February 2011.
  • Acar, Evrim, Daniel M. Dunlavy, Tamara G. Kolda, Morten Mørup, "Scalable Tensor Factorizations for Incomplete Data," Journal Article, Chemometrics and Intelligent Laboratory Systems, Vol. 106, No. 1, pp. 41–56, Accepted/Published March 2011.
  • Dunlavy, Daniel M., Tamara G. Kolda, W. Philip Kegelmeyer, "Multilinear algebra for analyzing data with multiple linkages ," Book, Graph Algorithms in the Language of Linear Algebra, December 2011.
  • Dunlavy, Daniel M., Tamara G. Kolda, Evrim Acar, "Temporal Link Prediction Using Matrix and Tensor Factorizations," Journal Article, ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 5, No. 2, pp. 1–27, Article No. 10, Accepted/Published February 2011.