Scholar
Edmond Chow
Google Scholar ID: jGqGKGMAAAAJ
Georgia Institute of Technology
Citations & Impact
Citations
5,572
H-index
26
i10-index
63
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- - November 2024: Paper presented at the 2024 Supercomputing Conference.
- - June 2024: Organized the 2024 International Conference on Preconditioning at Georgia Tech.
- - October 2023: Hua Huang received an Honorable Mention for the 2023 ACM-IEEE CS George Michael Memorial HPC Fellowship.
- - May 2022: Luke Erlandson defended his Ph.D. thesis and will start as a postdoc in CASC at LLNL.
- - October 2020: Paritosh Ramanan defended his Ph.D. thesis and is using his expertise in asynchronous distributed computing to create a startup company, Blockalytics.
- - June 2020: Jordi Wolfson-Pou successfully defended his Ph.D. thesis and is heading to LBNL for a postdoc.
- - April 2020: Hua Huang won the 2020 Sigma Xi Best M.S. Thesis Award.
- - March 2020: General interest article on preconditioning co-authored by Edmond Chow and Kees Vuik, published in SIAM News.
- - October 2019: Xin Xing successfully defended his Ph.D. thesis and will start a postdoc at UC Berkeley in July.
- - July 2018: Alex Lohse successfully defended his Ph.D. thesis.
- - April 2018: Paritosh Ramanan selected for a Georgia Tech Sam Nunn Predoctoral Fellowship, and Benson Ma won the College of Computing Marshall D. Williamson Fellowship.
- - December 2017: Benson Ma defended his M.S. CSE thesis.
- - February 2017: Planning to have a large showing of research from the group at the 2017 SIAM CSE Conference in Atlanta.
- - November 2016: Edmond Chow organized the Georgia Tech show at the annual ACM/IEEE SC (Supercomputing) conference.
Research Experience
- Position: Professor and Associate Chair
- Institution: School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology
- Research Focus: Numerical methods, particularly numerical linear algebra, for high-performance computers applied to scientific computing and data science problems.
Background
- Research Interests: High-performance computing, numerical linear algebra, scientific computing, and data science problems. Applications include PDE models, quantum chemistry, molecular dynamics, Brownian/Stokesian dynamics, inverse problems, data assimilation, uncertainty quantification, Gaussian processes, machine learning.
Miscellany
- Personal Interests: Seeking PhD students interested in high-performance computing with experience in GPU programming.
Co-authors