Ziyue Jiang
Senior Principal Scientist at Pfizer
San Diego, California, United States
Summary
🤩
Rockstar🎓
Top SchoolZiyue Jiang is a Senior Principal Scientist in San Diego with over a decade of hands-on expertise in oncology, molecular imaging, cancer immunology, and gene therapy, currently leading preclinical efforts at Pfizer. She combines deep technical skills—animal modeling, biomarkers, multi-modality in vivo imaging, antibody fragment production, and immunoassay development—with a track record of translating results into IND-ready documents and operational SOPs. At NantKwest and ImaginAb she built imaging programs, overhauled preclinical standards, and authored pivotal regulatory summaries, demonstrating both scientific rigor and program leadership. Her background in viral vector engineering and immune-focused assay development reflects a rare bridge between molecular pharmacology and translational oncology. Beyond the bench she contributes to open-source AI tooling around large-model parallelism, signaling a continual curiosity for computational methods that can accelerate biological research. Colleagues describe her as a focused, communicative doer who raises team standards while delivering actionable, high-quality data.
9 years of coding experience
11 years of employment as a software developer
Bachelor’s Degree, Biology/Biological Sciences, General, Bachelor’s Degree, Biology/Biological Sciences, General at Tsinghua UniversityDoctor of Philosophy (Ph.D.), Molecular Pharmacology, Doctor of Philosophy (Ph.D.), Molecular Pharmacology at University of California, Los Angeles
English, Chinese
Github Skills (7)
pytorch10
deep-learning10
parallelization10
python10
large-scale10
distributed-computing10
ai9
Programming languages (5)
Github contributions (5)
hpcaitech/ColossalAI
Mar 2022 - Jan 2023
Making large AI models cheaper, faster and more accessible
Role in this project:
ML Engineer
ML EngineerContributions:169 reviews, 52 commits, 87 PRs in 9 months
Contributions summary:Ziyue primarily focused on developing and modifying core components within the `colossalai` repository, which is designed for large AI model training. Their contributions involved adding functionalities to support tensor parallelism, specifically for the `Linear` and `Embedding` layers. These changes included adding features, addressing shape-related issues, and integrating distributed communication primitives. They also made code format and assertion changes to improve the library's robustness and correctness.
heterogeneous-trainingcolossal-aifinetuningdeep-learninginference
Wesley-Jzy/SJTU-COMPILER-LAB-2017
Jan 2018 - Jan 2022
Contributions:1 push, 1 branch in 4 years
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