Fan Chen
Research Scientist In Computer Vision at ByteDance
Portland, Oregon Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolFan Chen is a research scientist in computer vision at ByteDance with over a decade of experience developing large-scale multimodal training, vision-based recommendation models, and automated video production systems. He blends industrial impact—from optimized CV/ML pipelines on Intel hardware and multiple patented inventions—to academic depth as a former JAIST assistant professor and postdoc with ~45 peer-reviewed publications. His career spans applied roles across the US, Europe, and Japan, including contributions to a well-known Microsoft AI education repo where he debugged and improved core neural network components. Comfortable taking algorithms from research to production, he has repeatedly earned internal recognition for delivering high-impact features like TikTok Autocut and CTR improvements. He holds a PhD in Information Science and was a MEXT scholar, reflecting a long-standing track record of cross-cultural research and engineering leadership.
11 years of coding experience
14 years of employment as a software developer
BS, Computer Science, BS, Computer Science at Nanjing UniversityPh.D, Information Science, Ph.D, Information Science at Japan Advanced Institute of Science and TechnologyMS, Information Science, MS, Information Science at Tohoku University
Chinese, Japanese, english (toeic 935), French
Github Skills (10)
neural-network10
machine-learning10
deep-learning10
python10
numpy9
rnn-model8
faster-rcnn8
mask-rcnn8
n8
optimization7
Programming languages (5)
Github contributions (5)
microsoft/ai-edu
Jul 2020 - Feb 2021
AI education materials for Chinese students, teachers and IT professionals.
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 53 PRs, 26 comments in 6 months
Contributions summary:Fan primarily focused on debugging and improving the accuracy and functionality of neural network implementations. Their contributions involved fixing issues related to the return values of methods, such as fixing the missing return of accuracy in the Test method and batch output in the forward method, as well as adding parameters for the Optimizer. They made updates to several files related to the Neural Network and Dropout layers, suggesting they were debugging and refining core components of the deep learning models.
ai
qindagang/Data-Structures-and-Algorithm-Analysis-in-TS
Sep 2020 - Apr 2021
图文并茂、示例丰富、代码严谨，探寻数据结构与算法之美。
Contributions:1 release, 2 PRs, 161 pushes in 6 months
datastructures
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