[Nomination] Pratik Korat #92
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
Pratik Korat
Nominee Email
Nominee's GitHub or GitLab Handle
https://github.com/pratikkorat26
(Optional) Organization / Affiliation
San Jose State University
City, State/Province, Country
San Jose, CA, USA
Your Name
Pratik Korat
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?
As a long-time core user of PyTorch with 6–7 years of hands-on experience across domains—including Computer Vision, Natural Language Processing, Reinforcement Learning, and Tabular Data—I bring both depth and versatility to the table. My proficiency in applying PyTorch to real-world problems, combined with my role as a Teaching Associate for Deep Learning and Machine Learning at San José State University, puts me in a strong position to make an immediate and sustained impact as a PyTorch Ambassador.
My vision as an Ambassador is straightforward: grow the PyTorch community by enabling more developers, researchers, and students to adopt PyTorch not just as a framework, but as a mindset for rapid prototyping, reproducible research, and scalable deployment.
Here’s how I will contribute:
1. Community Engagement & Knowledge Sharing
I plan to actively contribute to the PyTorch ecosystem by:
Publishing hands-on tutorials that bridge theory and practical implementation in CV, NLP, RL, and tabular modeling.
Creating advanced walkthroughs for real-world problems, such as anomaly detection, fine-tuning LLMs, and multi-modal data processing.
Sharing open-source repositories with annotated notebooks and reproducible results to encourage learning by doing.
2. Event Hosting & Technical Workshops
Leveraging my university network and industry contacts, I will:
Organize PyTorch hackathons and coding sprints focused on applied AI challenges.
Host “PyTorch Deep-Dives” at universities and local tech meetups to demystify complex concepts like custom autograd functions, TorchScript, and model optimization techniques.
Conduct live coding sessions and AMA-style webinars featuring both foundational topics and bleeding-edge research.
3. Mentorship & Teaching
In my role as a Teaching Associate, I regularly mentor students who are transitioning from theoretical ML to real-world deep learning. As an Ambassador, I would scale this impact by:
Launching a peer-mentorship program for students and early-career developers looking to build a strong PyTorch portfolio.
Collaborating with faculty to integrate PyTorch-based labs into curriculum modules.
Helping students prepare for research internships and ML competitions with curated PyTorch learning tracks.
4. Forward-Looking Initiatives
I plan to push the envelope by:
Promoting PyTorch use in underrepresented areas such as tabular deep learning, causal inference, and simulation-based training.
Collaborating on open-source benchmarks and reproducibility efforts that align with PyTorch’s mission of transparency and research-grade tooling.
Advocating for responsible AI practices within the PyTorch ecosystem, including model interpretability and fairness audits.
With a strong combination of applied expertise, academic involvement, and community enthusiasm, I’m ready to contribute meaningfully to the PyTorch Ambassador Program. My commitment is not just to use PyTorch, but to build a stronger, more inclusive, and forward-thinking community around it.Any additional details you'd like to share?
In addition to my teaching and community involvement, I actively conduct research using PyTorch. Most recently, I submitted a research paper addressing bias and fairness challenges in federated learning, specifically applied to the healthcare sector. The work leverages PyTorch to implement distributed model training with fairness-aware optimization strategies, showcasing how the framework can be used to tackle critical real-world issues in sensitive domains like healthcare. This further reflects my commitment to applying PyTorch not just for performance, but also for ethical and responsible AI.