Jingkang Yang
Chief Technology Officer at Synvo AI
Singapore, Singapore
Summary
🤩
Rockstar🎓
Top SchoolJingkang Yang is a CTO and co-founder based in Singapore with a decade of experience turning computer vision research into production AI products. He holds PhD-level training in AI and computer vision from Nanyang Technological University (with research ties to Rice) and brings hands-on expertise in MLOps and backend engineering, evidenced by contributions to OpenOOD where he implemented Mahalanobis-distance scoring and clustering for robust out-of-distribution detection. At Synvo AI he combines technical leadership with startup-building skills, bridging model research, training pipelines, and scalable deployment. Comfortable moving between deep research and pragmatic engineering, he has a track record of refactoring training workflows and integrating advanced feature engineering into production systems.
10 years of coding experience
Doctor of Philosophy - PhD, Artificial Intelligence, Computer Vision, Doctor of Philosophy - PhD, Artificial Intelligence, Computer Vision at 新加坡南洋理工大学Doctor of Philosophy, Computer and Information Sciences, General, Doctor of Philosophy, Computer and Information Sciences, General at Rice UniversityBachelor of Science - BS, Telecommunications Engineering with Management, Bachelor of Science - BS, Telecommunications Engineering with Management at Queen Mary University of LondonBachelor of Engineering (B.Eng.), Telecommunications Engineering with Management, Bachelor of Engineering (B.Eng.), Telecommunications Engineering with Management at 北京邮电大学
Github Skills (7)
pytorch10
machine-learning10
anomaly-detection10
python10
clustering10
scikit-learn9
scikit9
Programming languages (5)
Github contributions (5)
Jingkang50/OpenOOD
Nov 2021 - Jan 2023
Benchmarking Generalized Out-of-Distribution Detection
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:51 reviews, 206 commits, 68 PRs in 1 year 1 month
Contributions summary:Jingkang's commits primarily focus on adding and modifying functions within the `openood/postprocessors/mds_tools.py` and `openood/trainers/udg_trainer.py` files. These modifications suggest a focus on implementing and integrating new techniques, specifically involving Mahalanobis distance scoring and clustering methods, within the generalized out-of-distribution detection framework. The user also appears to be involved in refactoring and integrating model training procedures related to these techniques, which also included defining various feature types and dimensional reduction. This indicates involvement in both back-end development and potential MLOps tasks.
benchmarkingoutlier-detectionrobustnessanomaly-detectionbenchmark
Jingkang50/jingkang50
Dec 2020 - Mar 2025
Contributions:6 pushes, 1 branch in 4 years 3 months
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