Team Assignments for Upcoming Virtual-Internships

Teams Based on MCQ Scores

We have extended our deadline, so more participants will be added on a rolling basis. Passing score is 8 out of 10. While we’ve accepted a couple of scores of 7, please ensure you thoroughly read the project details.

Foundational Projects:

Proceed to the code-along for your project and submit your replies to the specified tasks. If you encounter any issues, ask questions via the forum, and mentors will respond as soon as possible.

Advanced Projects:

We will contact you soon with details about the AI Evaluator and a kick-off preparatory meeting with our mentors.

Collaborative Opportunities:

Our projects are designed to encourage dynamic collaboration among teams within the same area. For example, the Foundational ML team can dive into NLP tasks highlighted by the Recommender team, and the Recommender team can compare their findings with those of the GNN-based Recommender team to develop a comprehensive study.

In addition, those who have honed their skills in R Shiny can collaborate with the advanced Bioinformatics team to implement sophisticated projects. Such cross-team interactions not only enhance learning but also lead to innovative solutions.

For the Survey of AI Coding Assistants, team members have the opportunity to familiarize themselves with specific AI coding assistants by working on tasks such as Text Classification using NLP or the PPI Visualization App. This hands-on approach provides a thorough understanding of AI tools and their applications in various projects.

Machine Learning

GNN-Based Recommender Systems

NLP Pipeline - Recommender Systems

Text Classification using NLP


Biomedical Knowledge Graph

PPI Visualization Graph

Survey of AI Coding Assistants