Yihong Chen
Technical Leader at greatalent technology inc
Beijing, Beijing, China
Summary
👤
Senior🎓
Top SchoolYihong Chen is a technical leader based in Beijing with nearly two decades of experience designing and improving circuit simulation and chip macromodeling tools for companies like Synopsys, Huada Empyrean, and Greatalent. He blends deep domain expertise in SPICE/Fast SPICE, mixed-signal and post-layout simulation with hands-on engineering and customer-facing product leadership, often translating complex customer requests into prioritized roadmaps and on-site integrations. At Greatalent he built a reliability platform around the Saber simulation tool and continues to consult on chip macro models, demonstrating a practical focus on product reliability and toolchain interoperability. Beyond EDA, he contributes to open-source projects in computer vision and annotation—implementing Deep Feature Flow in a CVPR2020 codebase and fixing Three.js-related issues in the widely used Scalabel annotation tool—showing versatility across ML and full-stack development. Known for bridging research, product, and engineering teams, he combines academic roots in navigation and automatic control with a pragmatic track record of shipping performance and integration improvements.
8 years of coding experience
15 years of employment as a software developer
硕士, Navigation and Automatic Control, 硕士, Navigation and Automatic Control at Beihang University
Chinese, English
Github Skills (16)
computer-vision10
pytorch10
javascript10
dataannotations10
front-end-development10
python10
data-annotation10
react10
data-integration9
integrations9
typescript-types9
typescript9
typescripts9
integrate9
threejs9
Programming languages (3)
Github contributions (5)
Scalsol/mega.pytorch
Mar 2020 - May 2021
Memory Enhanced Global-Local Aggregation for Video Object Detection, CVPR2020
Role in this project:
ML Engineer
ML EngineerContributions:24 commits, 2 PRs, 16 pushes in 1 year 1 month
Contributions summary:Yihong's primary contribution focused on implementing and integrating Deep Feature Flow (DFF) into the existing video object detection framework. This involved adding new modules related to DFF, modifying the backbone and flownet components, and updating the dataset and data loading pipelines. The user also incorporated motion-specific evaluation and visualization capabilities. These changes suggest a focus on enhancing the model's performance and providing tools for analyzing results within the video object detection domain.
memoryaggregationobject-detectionvideo-object-detectionvideo
scalabel/scalabel
May 2021 - May 2022
Scalabel: A versatile web-based visual data annotation tool
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:20 reviews, 98 commits, 35 PRs in 1 year
Contributions summary:Yihong primarily contributed to bug fixes and feature enhancements within the Scalabel project. They addressed deprecated interface issues by updating the Three.js library, fixed errors in dataset loading and exporting formats, and resolved problems related to vertex manipulation within the polygon tracking mode. The user also implemented project deletion functionality and added support for the new Scalabel data format.
data-annotationweb-basedvisual-dataannotation-toolversatile
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