[Nomination] <Ying-Jung Chen> #226
Description
Select one:
- I am nominating myself for the PyTorch Ambassador Program.
- I am nominating someone else to become a PyTorch Ambassador.
Please confirm that the nominee meets the following requirements:
- The nominee is 18 years of age or older.
- The nominee agrees to abide by the PyTorch Code of Conduct.
- The nominee agrees to comply with the Linux Foundation Antitrust Policy.
- The nominee meets at least one of the qualifications listed in the PyTorch Ambassador Program Requirements.
Nominee Name
Ying-Jung Chen
Nominee Email
Nominee's GitHub or GitLab Handle
(Optional) Organization / Affiliation
No response
City, State/Province, Country
Cupertino, CA
Your Name
Ying-Jung Chen
Your Email (Optional)
How has the nominee contributed to PyTorch?
- An active contributor to PyTorch repositories (e.g., commits, PRs, discussions).
- A speaker at PyTorch events or workshops.
- A PyTorch user group organizer or meetup host.
- A researcher or educator using PyTorch in academic work or training.
- An active leader in the PyTorch community with at least one year of experience in:
- Organizing events (virtual/in-person).
- Speaking at AI/ML conferences.
- Mentoring others in PyTorch.
- Creating technical content (e.g., blogs, videos, tutorials).
🏆 How Would the Nominee Contribute as an Ambassador?
Ying-Jung Chen would be an exceptional PyTorch Ambassador due to her deep expertise in LLMs, multi-agent systems, and scalable ML infrastructure, combined with her strong track record of open-source contributions and public engagement.
She is a seasoned AI Software Engineer with over 5 years of hands-on experience using PyTorch to develop and deploy production-ready AI systems in healthcare, climate science, and government sectors. Notably, she has led multi-agent LLM deployments and CUDA-accelerated model pipelines, highlighting her proficiency in both research-grade experimentation and real-world applications. Her work at the US Forest Service and Descartes Labs demonstrates how she leverages PyTorch in mission-critical settings—from wildfire forecasting to smart contract risk detection.
Ying-Jung has actively contributed to the open-source community through the MLCommons AIRR and
CIRISAI, and she serves as a reviewer for NeurIPS workshops, ensuring scientific rigor and inclusivity. As an Ambassador, she would expand this impact by mentoring early-career researchers, hosting community events and tutorials on advanced topics such as RAG systems and distributed model training with PyTorch, and fostering collaboration across academia, national labs, and open-source projects.
In addition, Ying-Jung has presented at conferences like SciPy and NeurIPS, and she is well-prepared to lead local meetups, contribute to PyTorch's community documentation, and create educational resources targeted at underrepresented communities. She is available immediately and committed to strengthening the ecosystem through mentorship, hands-on workshops, and collaborative innovation.Any additional details you'd like to share?
Ying-Jung Chen is not only a highly skilled AI engineer and researcher but also a passionate educator and open-source contributor. She actively engages with the global AI community through:
Conference Presentations:
PyData Seattle 2023 -"Let's program to flight the impact of climate change" : https://www.youtube.com/watch?v=IU4_mTUdER8
SciPy 2024 – “Employing the Strengths of Generative AI in Time Series Forecasting”
NeurIPS 2024 Workshop – “Stubble Burning Detection with Geospatial Foundation Models”
NeurIPS 2023–2025 – Serving as a reviewer for the Climate Change AI (CCAI) workshop
Publications:
arXiv 2025: “Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance”
Journal of Information Security 2025: “Information Security, Ethics, and Integrity in LLM Agent Interaction”
arXiv 2024: “Science Time Series: Deep Learning in Hydrology”
Open Source Contributions:
Contributed to CIRISAI: https://github.com/CIRISAI
Member of the MLCommons AIRR working group and AI Security group
Github: https://github.com/hydrogeohc
LinkedIn: https://www.linkedin.com/in/yj-elizabeth-chen
She is deeply committed to democratizing AI knowledge, with plans to launch a PyTorch-powered tutorial series for climate and geospatial AI, and to organize interdisciplinary workshops that bridge environmental science, public safety, and deep learning communities.