engelke ÄŦ tum DOT de
Technische Universität München; Informatik 25
Boltzmannstr. 3; 85748 Garching b. München; Germany
Alexis Engelke is a post-doctoral researcher at the Chair of Data Science and Engineering at Technical University of Munich (TUM), where he also earned his PhD (Dr. rer. nat.) in 2021. He conducts research on efficient processing of data on modern hardware architectures. Previous research areas include dynamic binary translation and optimization. His general research interests include (parallel) performance optimization, reverse engineering, and low-level aspects of modern processor architectures in general.
- Efficient Data Processing and Code Generation
- Dynamic Code Generation and Optimization
- Binary Translation and Instrumentation
- New/modern processor architectures
- Low-level aspects of modern processor architectures
- ... and more.
- Instrew: High performance binary translation and instrumentation based on LLVM. (2019–ongoing)
- Rellume: Fast lifter of x86-64/AArch64/RISC-V64 machine code to LLVM-IR. (2019–ongoing)
- Fadec (online version): A very fast and small decoder/encoder for x86 and x86-64. (2018–ongoing)
Former projects, no longer active:
- BinOpt: Library for self-guided binary specialization at runtime integrating an LLVM-based rewriter, DBrew, and Drob. (2016–2021)
- HimMUC: Cluster of AArch64-based SoCs for teaching/research. (2017–2022)
- Mandel-QPU: Mandelbrot computation for the Raspberry Pi GPU. (2016)
For a list of courses and supervised students, see here.
Publications and Talks
For a list of publications and talks, see here.