- Efficient and Scalable Linear Solver for Kernel Matrix Approximations Using Hierarchical Decomposition. Bachelor thesis, 2023 more…
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 more…
- Efficient and Scalable Kernel Matrix Approximations Using Hierarchical Decomposition. In: Intelligent Computers, Algorithms, and Applications. Springer Nature Singapore, 2024 more…
- Efficient and Scalable Kernel Matrix Approximation using Hierarchical Decomposition. Master thesis, 2022 more…
Posters
Talks and presentations
- Dynamic HPC resources for PinT: Algorithmic perspective. Parallel in time workshop 2024, 2024 more…
Research interests
- High Performance Computing
- Software Engineering
- Parallel-in-time methods
- Parallel Programming
- Numerics