Jianqing Zhang

Shanghai, Shanghai, China

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Summary

🤩

Rockstar

🎓

Top School

Jianqing Zhang is a PhD student in Computer Science at Shanghai Jiao Tong University with nine years of hands-on experience in federated learning, transfer learning, and recommender systems. He has contributed core algorithm implementations (e.g., FedProx) to the widely used PFLlib project and helped make federated learning easily runnable on personal machines. Jianqing’s internships and research stints span top industry and academic labs—ByteDance, Tencent, Alibaba, Tsinghua, KAUST—where he worked on privacy-preserving distributed ML, code LLM safety, on-device AI benchmarks, and synthetic dataset generation with papers at AAAI, CVPR, and JMLR. He values collaborative research aligned with federated learning’s principles and has built cross-institution collaborations with researchers at Tsinghua, Queen’s, and LSU. Outside of labs and code, he pursues photography, bringing a visual perspective to problem solving and an eye for detail in experimental design.

9 years of coding experience

Bachelor's degree Computer Science and Technology, Bachelor's degree Computer Science and Technology at Hangzhou Dianzi University

Doctor of Philosophy Computer Science, Doctor of Philosophy Computer Science at Shanghai Jiao Tong University

Chinese, English

Github Skills (6)

algorithm10

pytorch10

machine-learning-algorithms10

python10

federated-learning10

distributed-computing9

Programming languages (4)

C++TeXJupyter NotebookPython

Github contributions (5)

TsingZ0/PFLlib

Mar 2021 - Jan 2023

37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. www.pfllib.com/

Role in this project:

Back-end Developer & ML Engineer

Contributions:13 releases, 21 reviews, 144 commits in 1 year 10 months

Contributions summary:Jianqing contributed to the implementation of the FedProx algorithm, adding it to the `system/main.py` file and modifying other files like `flcore/optimizers/fedoptimizer.py` and `flcore/clients/clientprox.py`. This indicates involvement in developing the core federated learning algorithms. Furthermore, the user made adjustments to the configurations for this project. The primary focus appears to be on federated learning methodologies within the context of the project.

personalizedpythonsimulationrnnnon-iid

TsingZ0/LSPM

May 2020 - Mar 2021

Contributions:8 commits, 6 pushes, 1 branch in 9 months

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