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

Anonymous: Parallel and High Performance Computing for Time Series Big Data Processing. Masterarbeit, 2024 mehr…
Zhuoling Li: Efficient and Scalable Linear Solver for Kernel Matrix Approximations Using Hierarchical Decomposition. Bachelorarbeit, 2023 mehr…

Publications

Gaddameedi, Keerthi; Reiz, Severin; Neckel, Tobias; Bungartz, Hans-Joachim: Efficient and Scalable Kernel Matrix Approximations Using Hierarchical Decomposition. In: Communications in Computer and Information Science. Springer Nature Singapore, 2024 mehr…
Keerthi Gaddameedi: Efficient and Scalable Kernel Matrix Approximation using Hierarchical Decomposition. Masterarbeit, 2022 mehr…

Posters

Pierre-François Dutot, Jan Fecht, Keerthi Gaddameedi, Dominik Huber, Sergio Iserte, Michael Minion, Martin Schulz, Martin Schreiber, Valentina Schüller, Antonio J. Peña, and Olivier Richard: Leveraging Dynamic Resource Management in HPC. ISC 2024 Hamburg 2024 mehr…
Keerthi Gaddameedi, Severin Reiz, Tobias Neckel, Hans-Joachim Bungartz: Scalable Hierarchical Approximation of Dense Kernel Matrices. SIAM-PP 2024Baltimore mehr…
Keerthi Gaddameedi, Dominik Huber, Jan Fecht, Valentina Schuller, Martin Schreiber, Hans-Joachim Bungartz, Tobias Neckel: PFASST with dynamic resource management for large-scale applications. Parallel-in-Time workshop 2023, 2023Hamburg, Germany mehr…

Talks and presentations

Keerthi Gaddameedi, Dominik Huber, Martin Schreiber, Jan Fecht, Valentina Schueller, Michael Minion, Hans-Joachim Bungartz: 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

Currently teaching

Winter term 24/25

Previous teaching

Winter term 22/23

Master Practical Course - Scientific Computing - High-Performance Computing (IN2106, IN4230)