Abraham Chan
Postdoctoral Researcher at The University of British Columbia
Vancouver, British Columbia, Canada
Summary
👤
Senior🎓
Top SchoolAbraham Chan is a research-focused software engineer and PhD candidate specializing in ML reliability, robust and explainable ensembles, and compiler-driven resilience for safety-critical systems like autonomous vehicles and medical diagnosis. With 11 years of industry and academic experience, he blends deep compiler expertise—demonstrated by work on LLVM/Clang, a patent, and production-grade GPU/AI accelerator compiler optimizations—with practical tooling such as a TensorFlow/PyTorch LLVM-IR fault-injection framework (LLTFI). He has driven measurable results, including a 90% coverage error-propagation analysis and a 10x shader lowering speedup, and routinely teaches program analysis and software verification at UBC. Abraham’s background in data pipelines, testing automation, and approximate computing gives him a rare cross-disciplinary perspective that ties low-level systems work to real-world ML safety problems.
11 years of coding experience
12 years of employment as a software developer
Master of Applied Science (M.A.Sc.), Computer Software Engineering, Master of Applied Science (M.A.Sc.), Computer Software Engineering at The University of British Columbia
Github Skills (7)
llvm-ir9
cpp7
pytorch6
llvm6
tensorflow5
fault-injection5
computer-engineering3
Programming languages (4)
Github contributions (5)
DependableSystemsLab/LLTFI
Jun 2021 - Oct 2022
LLTFI is a tool, which is an extension of LLFI, allowing users to run fault injection experiments on C/C++, TensorFlow and PyTorch applications at the LLVM IR level. Please refer to the paper below. NOTE: If you publish a paper using LLTFI, please cite the following paper:
Contributions:17 reviews, 23 commits, 11 PRs in 1 year 4 months
cppfault-injectionllvm-irpytorchtensorflow
ubc-cpen400p-22w/classroom
Jan 2023 - Mar 2023
Contributions:13 commits, 13 pushes, 1 branch in 2 months
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