[Nomination] Ciro Russo #130
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
Ciro Russo
Nominee Email
Nominee's GitHub or GitLab Handle
cirorusso2910
(Optional) Organization / Affiliation
No response
City, State/Province, Country
Italy
Your Name
No response
Your Email (Optional)
No response
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?
As a PyTorch Ambassador, I would actively contribute by fostering knowledge-sharing within both academic and applied AI communities, particularly in the medical imaging and computational biology domains.
I plan to organize practical workshops and hands-on sessions focused on deep learning for healthcare, making PyTorch more accessible to early-career researchers and students.
Through my role as a university tutor and researcher, I already mentor students and guide them in building real-world AI systems using PyTorch—ranging from cytology applications to predictive models for public data challenges. I intend to scale this mentoring by launching open-source tutorials, challenge-based learning projects (e.g., medical image classification, multimodal fusion), and by promoting PyTorch adoption in interdisciplinary contexts like cultural heritage and smart cities.
I am also the author of GravityNet, a PyTorch-based architecture designed for robust small object detection in biomedical images. The project is openly available on GitHub and serves as a foundation for applied research and reproducible experimentation in computational pathology and related fields.
Finally, I would collaborate with existing community events (e.g., CVPL, university-led AI schools) to showcase PyTorch-based research pipelines, and support newcomers through public Q&A sessions, live demos, and informal meetups, both in-person and online.Any additional details you'd like to share?
As an active contributor in both academic research and higher education, I’ve published peer-reviewed articles leveraging PyTorch for tasks such as nuclei detection, cytology classification, and breast cancer imaging. My work spans international collaborations in brain imaging and oncology, and I often use PyTorch to prototype, benchmark, and deploy deep learning pipelines.
I also support educational outreach by tutoring in advanced training courses, where I help students develop PyTorch-based projects in areas like document AI and multimodal health diagnostics. Additionally, I’m developing open educational materials—such as Python packages, reproducible notebooks, and ML challenges (e.g., Formula 1 predictive modeling)—to engage students and early-stage researchers in applied AI.
I am committed to lowering entry barriers and promoting a responsible and inclusive AI culture through open science and community collaboration.