Research Professor Finder

Inspiration
Our inspiration came from witnessing students struggle to find research mentors aligned with their interests. Many talented students abandon research opportunities due to difficulty navigating faculty expertise, while professors miss out on passionate collaborators. We wanted to bridge this gap using intelligent matching powered by modern NLP techniques.

What It Does

1. Matches Students with Professors: Analyzes research interests (even with typos like "machiine learning") using advanced NLP to connect students with relevant faculty.
   
2. Provides Outreach Guidance: Generates personalized suggestions (e.g., "Mention NLP in your email – Prof. Smith specializes in this").
   
3. Simplifies Discovery: Filters search based on relevancy for efficiency.

How We Built It
We combined robust technologies in a 48-hour sprint:

• Frontend: React + TailwindCSS for a responsive interface.
  
• Backend: FastAPI with a search engine (Sentence Transformers).
  
• Database: SQLite with optimized schema for academic profiles.
  
• NLP Pipeline: NLTK, autocorrect for preprocessing. -Deepseek: For email templates.

Challenges We Ran Into

1. Data Scarcity: Manual cleaning of inconsistent faculty data.
   
2. Query Ambiguity: Handling misspellings ("machiine learning") and abbreviations ("AI" vs "Artificial Intelligence").
   
3. Performance Optimization: Balancing speed (<2s response) with semantic accuracy.
   
4. Team Coordination: Merging frontend/backend workflows under tight deadlines.
   

Accomplishments We’re Proud Of

1. Error-Resistant Search: The preprocessing pipeline corrects typos and expands academic jargon automatically.
   
2. Seamless Integration: Achieved real-time performance despite complex NLP operations.
   3.Webscraping:Webscraping: Developed a web scraper using Playwright to efficiently extract data from faculty websites and store it in an SQLite database.

What We Learned

1. NLP Nuances: Domain-specific models outperform generic tools for academic search.
   
2. Team Synergy: Agile practices like pair programming accelerated problem-solving.
   
3. Scalability Limits: Learned to optimize SQLite for 2k+ profiles efficiently.
   

What’s Next

1. Expansion: Add 50+ universities and grant database integration.
   
2. AI Enhancements:
   
   • LLM-generated outreach emails
     
   • Collaborative filtering ("Students like you worked with...")
     
3. Community Features: Research interest trending graphs and mentorship forums.
   
4. Deployment: Mobile app with AR campus navigation to professors’ offices.
   

We’re committed to becoming the "LinkedIn for Academia", democratizing research collaboration worldwide. 🚀

Built With

• deepseek
• fastapi
• python
• react.js
• tailwindcss
• threading