- Efficient and Scalable Linear Solver for Kernel Matrix Approximations Using Hierarchical Decomposition. Bachelorarbeit, 2023 mehr…
Keerthi Gaddameedi, M.Sc.
Technische Universität München
TUM School of CIT
Department of Computer Science
Boltzmannstrasse 3
85748 Garching
Germany
Office: MI 02.05.060
Tel: +49-89-289-18600
Mail: keerthi.gaddameedi (at) tum.de
Office Hours: by arrangement
Background
- Bachelors in Computer Science and Engineering, JNTU Hyderabad, India
- Masters in Informatics, TUM
- Currently a PhD Candidate, TUM
Open student Projects
If you are interested in the above topics and interested in doing a Thesis/HiWi/IDP/Guided research, write me an email with your transcripts and a CV.
I will then try to find out what topic might be suitable to you.
Student Theses
Publications
- Efficient and Scalable Kernel Matrix Approximations Using Hierarchical Decomposition. In: Intelligent Computers, Algorithms, and Applications. Springer Nature Singapore, 2024 mehr…
- Efficient and Scalable Kernel Matrix Approximations Using Hierarchical Decomposition. In: Intelligent Computers, Algorithms, and Applications. Springer Nature Singapore, 2024 mehr…
- Efficient and Scalable Kernel Matrix Approximation using Hierarchical Decomposition. Masterarbeit, 2022 mehr…
Posters
Talks and presentations
- Dynamic HPC resources for PinT: Algorithmic perspective. Parallel in time workshop 2024, 2024 mehr…
Research interests
- High Performance Computing
- Software Engineering
- Parallel-in-time methods
- Parallel Programming
- Numerics