Kunyuan Ding
Student
Summary
🤩
RockstarKunyuan Ding is a software engineering student with four years of practical experience focused on machine learning and out-of-distribution detection research. Based at Beijing University of Posts and Telecommunications, he contributes to open-source projects like OpenOOD, where he implemented PatchCore enhancements including random projections, coreset subsampling, and FAISS indexing to enable efficient pixel- and image-level anomaly scoring. He blends research-oriented rigor with engineering pragmatism, translating algorithmic ideas into production-ready postprocessors within a benchmarking framework. Notably, his work optimizes large-scale nearest-neighbor retrieval for anomaly detection, reflecting both systems thinking and hands-on ML engineering.
4 years of coding experience
Github Skills (7)
pytorch10
machine-learning10
anomaly-detection10
python10
faiss9
scikit8
scikit-learn8
Programming languages (2)
Github contributions (5)
Jingkang50/OpenOOD
Mar 2022 - Jun 2022
Benchmarking Generalized Out-of-Distribution Detection
Role in this project:
ML Engineer
ML EngineerContributions:44 commits, 8 PRs, 24 pushes in 2 months
Contributions summary:Kunyuan's commits primarily involve modifications to the `openood/postprocessors/patchcore_postprocessor.py` file, indicating work related to the PatchCore anomaly detection method. These changes include the addition of random projections, coreset subsampling, and faiss indexing. They are also working on pixel-level and image-level score calculations, with the aim of integrating patchcore within the openood framework for out-of-distribution detection.
benchmarkingoutlier-detectionrobustnessanomaly-detectionbenchmark
OmegaDING/AutoGenPopularVideo
Oct 2021 - Feb 2022
Contributions:12 commits, 7 pushes, 2 comments in 3 months
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