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Technical University of Munich

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.

  • Ahmad Traboulsi: Space-time parallelism for shallow water equations on the rotating sphere. Master thesis, 2025 more…
  • Armin Ettenhofer: Coupling Spectral Shallow-Water Equations and a 3D Finite Volume Solver to Simulate Complex Geometry. Master thesis, 2025 more…
  • Kevin Adameit: Parameter Evaluation of the Parallel Full Approximation Scheme in Space and Time (PFASST). Bachelor thesis, 2025 more…
  • Anonymous: Parallel and High Performance Computing for Time Series Big Data Processing. Master thesis, 2024 more…
  • Mohamed Aziz Kara borni: Scalable Kernel Matrix Inversion using Hierarchical Low-Rank Approximations. Bachelor thesis, 2024 more…
  • Tobias Eppacher: Parallel-in-Time Integration with preCICE. Bachelor thesis, 2024 more…
  • Zhuoling Li: Efficient and Scalable Linear Solver for Kernel Matrix Approximations Using Hierarchical Decomposition. Bachelor thesis, 2023 more…

  • 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 more…
  • Keerthi Gaddameedi: Efficient and Scalable Kernel Matrix Approximation using Hierarchical Decomposition. Master thesis, 2022 more…

  • Hans-Joachim Bungartz, Keerthi Gaddameedi, Dominik Huber, Tobias Neckel, Martin Schreiber, Martin Schulz: Adaptive parallel-in-time integration for hyperbolic PDEs. HPCAsia + SuperComputingAsia 2026 2026Osaka, Japan more…
  • 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 more…
  • Keerthi Gaddameedi, Severin Reiz, Tobias Neckel, Hans-Joachim Bungartz: Scalable Hierarchical Approximation of Dense Kernel Matrices. SIAM-PP 2024Baltimore more…
  • 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 more…

  • Keerthi Gaddameedi, Hans-Joachim Bungartz, Dominik Huber, Tobias Neckel, Martin Schulz, Martin Schreiber: Dynamic resource adaptivity for iterative parallel-in-time methods. DYNRESHPC2026 : 5th Workshop on Dynamic Resources in HPC, 2026 more…
  • Keerthi Gaddameedi, Hans-Joachim Bungartz, Dominik Huber, Tobias Neckel, Martin Schulz, Martin Schreiber: Adaptive PFASST for Shallow Water Equations on the Rotating Sphere. SIAM Conference on Parallel Processing for Scientific Computing (PP26), 2026 more…
  • 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 more…

Research interests

  • High Performance Computing
  • Software Engineering
  • Parallel-in-time methods
  • Parallel Programming
  • Numerics

Currently teaching

Summer term 2026

 

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