Developed the backend for Taxi for KAIST, a web and mobile service enabling
KAIST members to easily find taxi ride-shares based on time and location. Designed and
implemented RESTful APIs, real-time chat using WebSocket, and integrated MongoDB and Redis
for efficient data handling and caching.
Designed and led Taxi-Analytics, an ongoing data analysis project (2024–Present) that evaluates the impact of events, promotional campaigns, and overall service usage of Taxi for KAIST. Utilized statistical methods such as histograms, KDE, and Regression Analysis to assess service effectiveness and provide actionable insights for future strategic directions.
Led the design and development of Taxi Dev Center, a Docker-based platform for
Taxi for KAIST that provides isolated, scalable dev environments via VPN and private DNS
server.
Architected the system to streamline developer onboarding and deployment.
Conducted a case study analyzing the role of digital media and misinformation during the 2024 U.S. presidential election, focusing on political engagement driven by celebrity and CEO endorsements. Utilized LLM for fact-checking Trump’s tweets, revealing that misinformation posts received significantly higher public engagement than factual ones.