Tim Davis
Professor at Texas A&M University
College Station, Texas, United States
Summary
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Rockstar🎓
Top SchoolTim Davis is a professor of computer science and engineering at Texas A&M with 19 years in academia and decades more in numerical linear algebra and high-performance computing. He is the lead author and maintainer of the widely used SuiteSparse library, contributing deep expertise in sparse matrix algorithms, QR factorization, and performance optimization. His career spans faculty appointments at Texas A&M and the University of Florida, a visiting professorship at Stanford, and postdoctoral work on parallel algorithms at CERFACS. Trained with a PhD in Electrical Engineering from UIUC, he combines rigorous theoretical foundations with hands-on kernel-level and memory-management improvements that have practical impact in scientific computing.
19 years of coding experience
2 years of employment as a software developer
BS, Electrical Engineering, BS, Electrical Engineering at Purdue UniversityUniversity of Illinois Urbana-Champaign
Stackoverflow
41reputation
4kreached
1answer
0questions
Github Skills (10)
c-language10
cprogramming-language10
math10
numerical-methods10
linear-algebra10
mathematica10
fortran9
sparse-matrix6
solver6
matlab6
Programming languages (11)
Github contributions (5)
DrTimothyAldenDavis/SuiteSparse
Oct 2019 - Jan 2023
The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University.
Role in this project:
Backend Developer
Backend DeveloperContributions:121 releases, 28 reviews, 631 commits in 3 years 3 months
Contributions summary:Tim contributed significantly to the development and maintenance of the SuiteSparse QR factorization library, as indicated by the version updates and code modifications across multiple files. Their work focused on improving the library's functionality, fixing bugs, and optimizing performance, evident in the changes to files related to kernel invocations, memory management, and test procedures. These commits demonstrate a deep understanding of linear algebra, sparse matrix operations, and numerical computation.
mathematicssparsematrixmongoosesuitesparse
GraphBLAS/LAGraph
Jan 2019 - Jan 2023
This is a library plus a test harness for collecting algorithms that use the GraphBLAS. For test coverage reports, see https://graphblas.org/LAGraph/ . Documentation: https://lagraph.readthedocs.org
Contributions:38 releases, 27 reviews, 1215 commits in 4 years
graphblasreportstest-harnesstestingcoverage-reports
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