Zifan Jiang
Student Researcher at University of Zurich
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolZifan Jiang is a researcher-engineer with 13 years of software experience who now focuses on sign language processing as a Student Researcher at Google DeepMind and a PhD candidate in computational linguistics at the University of Zurich. His background spans applied frontend engineering for high-profile platforms (Zhihu, Starbucks China, World Bank Open Data) to scientific programming on phonetics and phonology projects, giving him a rare blend of production-grade product delivery and rigorous research practice. He has international research exposure, including a visiting stint at Oxford’s Visual Geometry Group, and works at the intersection of multimodal machine learning, linguistics, and accessibility. Trained in computer science and data science at ETH Zürich and the University of Zurich, he brings strong engineering discipline to experimental systems and reproducible research. Notably, his career path shows a shift from leading large-scale web frontends to building models and tools that make signed languages more accessible, reflecting both technical breadth and a user-centered drive.
12 years of coding experience
7 years of employment as a software developer
Master's degree, Data Science, Master's degree, Data Science at University of ZurichBachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at Nanjing UniversityMaster's degree, Computer Science, Master's degree, Computer Science at ETH Zürich
Github Skills (70)
textarea10
browser10
rich-text10
draft-js10
editors10
contenteditable10
editor9
javascript9
pose9
datasets9
wysiwyg9
rte9
rich-text-editor9
react9
text-editor9
Programming languages (6)
Github contributions (5)
J22Melody/subtitle_align
Jan 2023 - Mar 2025
Subtitle alignment for sign language video
Contributions:2 PRs, 32 pushes, 2 branches in 2 years 2 months
video-languagesignalignmentsign-languagevideo
J22Melody/fairseq
Mar 2023 - Mar 2025
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Contributions:8 reviews, 11 PRs, 110 pushes in 2 years
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