Keerthi Gaddameedi

Keerthi Gaddameedi is a PhD candidate at the Department of Computer Science, Technische Universität München (TUM), affiliated with the Chair of Scientific Computing in Computer Science (SCCS). Their research focuses on High Performance Computing, Parallel Programming, and Numerics, with contributions to scalable kernel matrix approximation techniques. They actively teach courses such as Modelling and Simulation, Numerical Algorithms for HPC, and Advanced Programming. Keerthi has advised multiple student theses on topics like parallel-in-time integration and kernel matrix inversion.

Education: Bachelors in Computer Science and Engineering from JNTU Hyderabad (India), Masters in Informatics from TUM. Current PhD research involves hierarchical decomposition methods for dense kernel matrices. Teaching roles span from 2022 to 2025, covering both undergraduate and graduate courses in scientific computing. Their work has been presented at venues like SIAM-PP 2024 and ISC 2024, focusing on dynamic resource management for parallel-in-time methods. Open projects include high-performance computing for time-series data and scalable linear solvers.