Scholar
Alexander Brauckmann
Google Scholar ID: h7DKhbMAAAAJ
University of Edinburgh
Citations & Impact
Citations
228
H-index
7
i10-index
5
Publications
17
Co-authors
0
Publications
Cited
0
Resume (English only)
Academic Achievements
- [{'Paper': 'STENSO: Tensor Program Superoptimization through Cost-Guided Symbolic Program Synthesis', 'Authors': "A. Brauckmann, A. Chaube, J. W. de Souza Magalhães, E. Polgreen, M. O'Boyle", 'Conference': "CGO '26"}, {'Paper': 'Tensorize: Fast Synthesis of Tensor Programs from Legacy Code using Symbolic Tracing, Sketching and Solving', 'Authors': "A. Brauckmann, L. Jaulmes, J. W. de Souza Magalhães, E. Polgreen, M. O'Boyle", 'Award': 'Distinguished Paper Award', 'Conference': "CGO '25"}, {'Paper': 'Guided Tensor Lifting', 'Authors': "Y. Li, J. W. de Souza Magalhães, A. Brauckmann, M. O'Boyle, E. Polgreen", 'Conference': "PLDI '25"}, {'Paper': 'DFA-Net: A Compiler-Specific Neural Architecture for Robust Generalization in Data Flow Analyses', 'Authors': "A. Brauckmann, A. Faustino da Silva, G. Synnaeve, M. O'Boyle, J. Castrillon, H. Leather", 'Conference': "CC '25"}, {'Paper': 'Guess, Measure & Edit: Using Lowering to Lift Tensor Code', 'Authors': "J. W. de Souza Magalhães, J. Woodruff, J. Armengol-Estapé, A. Brauckmann, L. Jaulmes, E. Polgreen, M. O'Boyle", 'Conference': "PACT '25"}, {'Paper': 'MlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis', 'Authors': "A. Brauckmann, E. Polgreen, T. Grosser, M. O'Boyle", 'Conference': "PACT '23"}, {'Paper': 'Rewriting History: Repurposing Domain-Specific CGRAs', 'Authors': "J. Woodruff, T. Koehler, A. Brauckmann, Chris Cummins, Sam Ainsworth, M. O'Boyle", 'Conference': "arXiv '23"}, {'Paper': 'ExeBench: an ML-scale dataset of executable C functions', 'Authors': "J. Armengol-Estapé, J. Woodruff, A. Brauckmann, J. W. de Souza Magalhães, M. O'Boyle", 'Conference': "MAPS '22"}, {'Paper': 'Polygym: Polyhedral Optimizations as an Environment for Reinforcement Learning', 'Authors': 'A. Brauckmann, A. Goens, J. Castrillon', 'Conference': "PACT '21"}, {'Paper': 'Compiler-based Graph Representations for Deep Learning Models of Code', 'Authors': 'A. Brauckmann, A. Goens, S. Ertel, J. Castrillon', 'Conference': "CC '20"}, {'Paper': 'ComPy-Learn: A Toolbox for Exploring Machine Learning Representations for Compilers', 'Authors': 'A. Brauckmann, A. Goens, J. Castrillon', 'Conference': "FDL '20"}, {'Paper': 'A Case Study on Machine Learning for Synthesizing Benchmarks', 'Authors': 'A. Goens, A. Brauckmann, S. Ertel, C. Cummins, H. Leather, J. Castrillon', 'Conference': "MAPS '19"}]
Background
- Research Interests: Compilers, Machine Learning, Program Synthesis; Field: Computer Science; Summary: Working at the intersection of compilers, machine learning, and program synthesis to make tensor programs more efficient.
Miscellany
- Skills: Programming (C, C++, Python), Compilers (JAX, MLIR, PyTorch), Machine Learning (Transformer Models, Graph Neural Network Models, Performance Profiling), Other (Linux, Git, Scrum, Table Tennis)
Co-authors