reSearch

Inspiration

The inspiration for reSearch came from seeing how difficult it can be for students to connect with research opportunities, especially those just beginning their academic journeys. Many students are passionate about research but lack the right networks or resources to find positions, while professors and researchers often struggle to find the right candidates. reSearch aims to bridge this gap, making research opportunities more accessible and fostering collaboration within the academic community.

What it does

reSearch is a platform that connects aspiring researchers, such as undergraduate and graduate students, to research opportunities on campus. Students create profiles showcasing their skills, research interests, and previous experience, while researchers (professors or grad students) create postings for available positions or projects. reSearch matches students to relevant projects based on skill, interest, and degree level, ensuring a good fit for both parties and fostering a smoother application process.

How we built it

We built reSearch using React for the frontend, creating an intuitive interface where users can easily create profiles, browse research postings, and view matched opportunities. For the backend, we used Python to manage the database, ensuring efficient data storage and retrieval. Additionally, we integrated the OpenAI API to add generative AI capabilities, enabling smart recommendations. Our team collaborated on different aspects, from front-end design to backend database management, and integrated AI features to enhance the user experience.

Challenges we ran into

One of the main challenges was developing a matching algorithm that accurately considers various factors like skills, experience level, and research interests. Ensuring data security and privacy for both students and researchers also required special attention. Additionally, creating a seamless user interface that would be both intuitive for students and functional for researchers took significant design effort.

Accomplishments that we're proud of

We're proud of the matching algorithm, which effectively connects students to research projects that fit their backgrounds and interests. Additionally, our team was able to create a functional prototype within a short timeframe, complete with user profiles, project postings, and a working search feature. Successfully balancing the different tech stacks and collaborating as a team was also a highlight.

What we learned

Through this project, we learned a lot about the intricacies of building a recommendation engine, as well as best practices for frontend-backend integration. We also gained insight into managing user data securely and ensuring a positive user experience. Working under time constraints taught us valuable lessons in project management and teamwork.

What's next for reSearch

Next, we aim to expand reSearch by adding more filtering options for users, including location and specific research interests. We’d also like to integrate feedback and rating features so that students and researchers can evaluate their experiences, creating a system of accountability and improvement. We hope to implement machine learning to enhance the accuracy of our recommendation engine and to expand the platform to other campuses, connecting even more students to valuable research opportunities.

Built With

• csv
• javascript
• openai
• python
• react