Education
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University of Edinburgh PhD in Computer Science, Topic: Tensor Program Synthesis, Advisor: Michael O'Boyle |
2022 – 2025 |
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Technische Universität Dresden Diplom (MSc) in Computer Science Most Outstanding Thesis Award 🏆 |
2020 |
Work Experience
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Meta (PyTorch Group) Research Scientist Intern |
Dec 2024 – Apr 2025 |
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AMD (AI Engine Group) Deep Learning Compiler Intern |
Jun 2023 – Sep 2023 |
Software
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Tensorize Program synthesis based tensor compiler, achieving avg. 4.1x speedup by lifting code to PyTorch, JAX and HLO. Novel top-down program synthesis algorithm with near linear scalability. Code |
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MLIR Synth MLIR program synthesizer, synthesizes equivalent programs in high-level MLIR dialects, like LinAlg and HLO. Code |
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PolyGym Exploring valid loop transformations in polyhedral model with reinforcement learning, finding schedules with avg. 1.83x speedup over Polly/ISL. Code |
Publications
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Tensorize: Fast Synthesis of Tensor Programs from Legacy Code using Symbolic Tracing, Sketching and
Solving. A. Brauckmann, L. Jaulmes, J. W. de Souza Magalhães, E. Polgreen, M. O'Boyle Distinguished Paper Award 🏆 Paper, Code |
CGO '25 |
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Guided Tensor Lifting. Y. Li, J. W. de Souza Magalhães, A. Brauckmann, M. O'Boyle, E. Polgreen Paper |
PLDI '25 |
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DFA-Net: A Compiler-Specific Neural Architecture for Robust Generalization in Data Flow Analyses. A. Brauckmann, A. Faustino da Silva, G. Synnaeve, M. O'Boyle, J. Castrillon, H. Leather Paper, Code |
CC '25 |
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MlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis. A. Brauckmann, E. Polgreen, T. Grosser, M. O'Boyle Paper, Code |
PACT '23 |
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Rewriting History: Repurposing Domain-Specific CGRAs. J. Woodruff, T. Koehler, A. Brauckmann, Chris Cummins, Sam Ainsworth, M. O'Boyle Paper, Code |
arXiv '23 |
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ExeBench: an ML-scale dataset of executable C functions. J. Armengol-Estapé, J. Woodruff, A. Brauckmann, J. W. de Souza Magalhães, M. O'Boyle Paper, Code |
MAPS '22 |
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Polygym: Polyhedral Optimizations as an Environment for Reinforcement Learning. A. Brauckmann, A. Goens, J. Castrillon Paper, Code |
PACT '21 |
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Compiler-based Graph Representations for Deep Learning Models of Code. A. Brauckmann, A. Goens, S. Ertel, J. Castrillon Paper, Code |
CC '20 |
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ComPy-Learn: A Toolbox for Exploring Machine Learning Representations for Compilers. A. Brauckmann, A. Goens, J. Castrillon Paper, Code |
FDL '20 |
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A Case Study on Machine Learning for Synthesizing Benchmarks. A. Goens, A. Brauckmann, S. Ertel, C. Cummins, H. Leather, J. Castrillon Paper |
MAPS '19 |
Skills
| Programming | C, C++, Python |
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| Compilers | JAX, MLIR, PyTorch |
| Machine Learning | Transformer Models, Graph Neural Network Models, Performance Profiling |
| Other | Linux, Git, Scrum, Table Tennis |