- Harry S. Truman Fellow
- Sandia National Laboratories – Livermore, CA
*I have recently completed my Truman Fellowship and am now in the Computer Science department at Wake Forest University.
Please see my current webpage
for more recent information.
Short Bio (CV)
Grey Ballard was a Truman Fellow at Sandia National Labs in Livermore, CA.
He received his PhD in 2013 from the EECS Department at the University of California Berkeley under advisor James Demmel.
He received his BS in math and computer science at Wake Forest University in 2006 and his MA in math at Wake Forest in 2008.
His research interests include numerical linear algebra, high performance computing, and computational science, particularly in developing algorithmic ideas that translate to improved implementations and more efficient software.
His work has been recognized with the SIAM Linear Algebra Prize, three conference best paper awards, at SPAA, IPDPS, and ICDM, the C.V. Ramamoorthy Distinguished Research Award at UC Berkeley, and the ACM Doctoral Dissertation Award - Honorable Mention.
Recent and Selected Papers
A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization.
Ramakrishnan Kannan, Grey Ballard, and Haesun Park.
Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. pp. 9:1-9:11. 2016.
Diamond Sampling for Approximate Maximum All-Pairs Dot-Product Search.
Grey Ballard, Tamara G. Kolda, Ali Pinar, and C. Seshadri.
Proceedings of the 2015 IEEE International Conference on Data Mining. pp. 11-20. 2015.
- Awarded the Best Paper Prize
Communication lower bounds and optimal algorithms for numerical linear algebra.
Grey Ballard, Erin Carson, James Demmel, Mark Hoemmen, Nicholas Knight, and Oded Schwartz.
Acta Numerica. Volume 23, pp 1-155. 2014.
Minimizing Communication in Linear Algebra.
Grey Ballard, James Demmel, Olga Holtz, and Oded Schwartz.
SIAM Journal on Matrix Analysis and Applications. Volume 32, Issue 3, pp 866-901. 2011.
- Awarded the SIAM Linear Algebra Prize
Avoiding Communication in Dense Linear Algebra.
PhD Thesis. EECS Department, University of California Berkeley. 2013.
- Awarded the ACM Doctoral Dissertation Award - Honorable Mention
"Diamond Sampling for Approximate Maximum All-Pairs Dot-Product Search."
Presented at the International Conference on Data Mining (ICDM)
in November 2015 in Atlantic City, NJ.
"Hypergraph Partitioning for Parallel Sparse Matrix-Matrix Multiplication."
Presented at the SIAM Conference on Applied Linear Algebra (LA)
in October 2015 in Atlanta, GA.
"Parallel Tensor Compression for Large-Scale Scientific Data."
Presented at the Development of Modern Methods for Linear Algebra (DMML) Workshop
in October 2015 in Berkeley, CA.
"Algorithmic Improvements for Dense Symmetric Tridiagonalization."
Presented at the International Workshop on Eigenvalue Problems:
Algorithms, Software, and Applications (EPASA)
in September 2015 in Tsukuba, Japan.