Min Cho
Deep Learning Software Engineer at Intel Corporation
California, United States
Summary
🤩
Rockstar🎓
Top SchoolMin Cho is a Deep Learning Software Engineer based in California with four years of experience bridging research and production-grade ML systems. Currently at Intel, Min focuses on performance engineering for PyTorch and Intel extensions, contributing low-level compiler decompositions, CPU optimizations, and production deployment improvements that accelerate XPU and CPU workloads. Their background includes research roles at Brown and Harvard where they developed novel neural architectures (e.g., Q-Net and Graph2Seq) and explored latent-space regularization and genomic algorithms, demonstrating a mix of theoretical insight and practical implementation. An active open-source contributor to flagship projects like pytorch/pytorch and pytorch/serve, Min brings a rare combination of compiler-level optimization skills, MLOps experience, and academic rigor—often improving both performance and deployability in the same patch.
4 years of coding experience
2 years of employment as a software developer
Torrey Pines High SchoolBachelor of Science, Computational Biology, Computer Science Track, 4.0/4.0 (Junior and Senior years), Bachelor of Science, Computational Biology, Computer Science Track, 4.0/4.0 (Junior and Senior years) at Brown University
English, Korean
Github Skills (32)
pytorch10
docker10
c-language10
python10
intel10
cpu10
machine-learning10
dockers10
cicd10
mlops10
deep-learning10
performance-optimization10
build-automation10
quantization10
cprogramming-language10
Programming languages (3)
Github contributions (5)
pytorch/serve
Nov 2021 - Aug 2022
Serve, optimize and scale PyTorch models in production
Role in this project:
MLOps Engineer
MLOps EngineerContributions:72 reviews, 16 commits, 20 PRs in 9 months
Contributions summary:Min's contributions primarily revolve around enhancing the build and deployment process for the `pytorch/serve` repository, focusing on integrating Intel's IPEX extensions for PyTorch. They introduced options to build Docker images with IPEX, integrated core pinning functionality for CPU workers, and added improvements for launching and managing workers. Their work involved modifications to build scripts, configuration files, and test infrastructure.
pytorchmachine-learningmlopsservingdocker
pytorch/pytorch
Oct 2022 - Jan 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:114 reviews, 79 commits, 46 PRs in 3 months
Contributions summary:Min's commits primarily focus on enhancing the performance and functionality of PyTorch, particularly within the context of XPU (Intel GPU) and CPU environments. They contributed to the Inductor compiler by adding decompositions for various mathematical operations, including `aten.uniform_`, `aten.exponential_`, `aten.cauchy_`, `aten.geometric_`, `aten.log_normal_`, and `aten.tan`, thereby improving overall code generation capabilities. Furthermore, the user optimized CPU batch normalization and fixed issues related to random number generation and distribution implementations, demonstrating a focus on core library performance.
gpu-accelerationneural-networkpythonautogradgpu
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