Matchathon

Matchathon matches first-year PhD students to faculty mentors based on mutual research interests to facilitate mentorship connections within academic programs.

Key Features:

• Algorithmic matching: Employs an algorithmic approach that matches students and faculty using detailed profiles of research interests and prioritizes compatibility in academic pursuits.
• Event organization support: Generates structured match schedules for Matchathon events where students meet multiple potential faculty mentors individually based on mutual interests.

Scientific Applications:

• Mentor-mentee pairing: Facilitates identification of compatible mentor-mentee relationships in programs with large faculty bodies by aligning student and faculty research interests.
• Student retention and satisfaction: Aims to improve student retention and satisfaction by increasing the likelihood that students find mentors whose research interests align with their own.

Methodology:

Leverages an algorithmic matching approach using detailed research-interest profiles, prioritizes compatibility, and is implemented as an R Shiny application that organizes structured Matchathon events.