Tengqi Ye
Applied AI Researcher at Articul8 AI
United States
Summary
👤
Senior🎓
Top SchoolTengqi Ye is an applied AI researcher and senior machine learning engineer with 10 years' experience building end-to-end ML systems across computer vision, NLP, multimodal and generative AI. He has led teams and shipped production services at companies including Wish, ByteDance and Akulaku, owning systems from OCR and face verification to image search and content moderation. Tengqi combines deep research roots (PhD, best paper award, multiple peer-review roles and a Google Scholar profile) with hands-on deployment expertise using vector databases and NVIDIA Triton. He contributes to open-source document-analysis tooling (notably improvements to PICK-pytorch data pipelines) and is comfortable taking solo projects from dataset processing to production. Colleagues rely on him for practical modelization of messy real-world signals and for translating cutting-edge research into scalable microservices.
10 years of coding experience
7 years of employment as a software developer
Bachelor’s Degree, Computer Software Engineering, grade 90 (ranking 10/300), Bachelor’s Degree, Computer Software Engineering, grade 90 (ranking 10/300) at Northeastern University (CN)Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Dublin City University
Github Skills (7)
data-preprocessing10
data-loading10
pytorch10
machine-learning10
python10
graph-convolutional-networks9
computer-vision9
Programming languages (7)
Github contributions (5)
wenwenyu/PICK-pytorch
Sep 2020 - Jun 2022
Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 24 commits, 15 PRs in 1 year 9 months
Contributions summary:Tengqi primarily contributed to the project by modifying the data loading and processing pipelines. The commits show improvements to the `Document` class, including fixes for bounding box handling and relation feature calculations, indicating a focus on refining the data preparation stage for the document analysis task. Furthermore, the user added scripts to process DocBank datasets, expanding the project's data handling capabilities. The changes also included adjustments to training scripts and model configurations, signifying involvement in model training and potentially debugging.
graph-learningextractionimproveddocument-analysispick
tengerye/activity_recognition_survey
Sep 2019 - Sep 2019
Contributions:15 commits, 14 pushes, 1 branch in 6 days
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