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Apply for community grant: Academic project (gpu and storage)
Project Name: Next-Gen Curriculum Optimizer
Problem:
University course catalogs and prerequisite chains are incredibly complex. Students struggle to build optimal degree plans that match their career goals, often leading to delayed graduation or wasted credits.
Solution:
This is an open-source tool to solve that problem. It's an "AI Academic Advisor" with a React frontend and a Python (FastAPI) backend.
Students input their profile (completed courses, GPA, career interests like "AI/ML"). The backend then generates a complete, validated, semester-by-semester degree plan.
How Hugging Face Is Used (Resource Justification):
The HF ecosystem is critical for the "AI" part of this project.
GPU (For Model Inference): The backend uses two HF model types:
Sentence Transformer (
BAAI/bge-large-en-v1.5): I create vector embeddings for all course descriptions. This lets students find relevant electives semantically (e.g., searching for "game dev" finds "graphics" and "physics simulation" courses).LLM (
meta-llama/Llama-3.1-8B-Instruct): This model analyzes the student's profile against the complex graph of prerequisites to generate a reasoned, human-readable plan. The GPU is essential to make this inference fast enough for a live web app.
Persistent Storage: This is needed to store the pre-processed university curriculum graph (as a
.pklfile) and to cache the downloaded HF models, preventing re-downloads on startup.
This is an academic project that I started during my masters in Computer Science at Northeastern University & I want to see it complete now that I have graduated & working on finding a job while on F1-OPT. I am working on this with a distinguished professor who specializes in curriculum design. The goal is to deploy this as a free, open-source tool for my student community and create a framework other schools can adapt.