Mingrui Liu
Assistant Professor at George Mason University
Virginia, United States
Summary
🤩
Rockstar🎓
Top SchoolMingrui Liu is an assistant professor at George Mason University with nine years of experience bridging machine learning, optimization, and deep learning research. He earned a Ph.D. in Computer Science from the University of Iowa and has held research positions at Boston University, IBM, and Alibaba's DAMO Academy, where he contributed to practical deep learning systems. His open-source work includes significant engineering on OFA (ICML 2022) image-generation pipelines and extending a Chinese CLIP implementation to support large ViT models, reflecting strength in cross-modal learning and production-ready tooling. Mingrui combines theoretical rigor with hands-on model engineering, often focusing on data preprocessing, model integration, and reproducible inference artifacts. Based in Virginia, he brings a mathematician’s precision from Shanghai Jiao Tong University to applied ML challenges, with a knack for turning cutting-edge research into usable code.
9 years of coding experience
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of IowaMathematics, Mathematics at Shanghai Jiao Tong University
Chinese, English
Github Skills (14)
computer-vision10
pytorch10
image-text10
deep-learning10
image-generation10
multimodal10
image-annotation10
clip10
python10
pre-trained-model10
machine-learning9
nlp8
transformers8
ch7
Programming languages (3)
Github contributions (5)
OFA-Sys/Chinese-CLIP
Jul 2022 - Jan 2023
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 3 PRs, 5 pushes in 6 months
Contributions summary:Mingrui primarily focused on modifying and extending the `cn_clip` library, a Chinese version of CLIP for cross-modal retrieval. Their contributions included refactoring code and adding API usage examples, and updating the code to support new models like ViT-L-14, ViT-H-14, and RN50. Furthermore, the user fixed a bug related to loading the RN50 model and updated the setup file.
chineseretrievalrepresentationtransformerscontrastive-loss
OFA-Sys/OFA
Feb 2022 - Jun 2022
Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Role in this project:ML Engineer
ML EngineerContributions:28 commits, 3 PRs, 14 pushes in 3 months
Contributions summary:Mingrui primarily contributed to the image generation aspects of the OFA repository, focusing on the integration and use of a CLIP model for image evaluation and generation. They added a notebook for caption inference and uploaded a text-to-image inference shell. The user also made several changes to datasets and scripts, as well as fixed bugs related to the image generation pipeline, including the data preprocessing steps and model configurations. Their work appears focused on building and refining the image generation capabilities.
chinesesequencevisual-question-answeringframework-learningicml
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