The 2024 Columbia-Barnard Undergraduate Computer and Data Science Research Fair was a showcase of what happens when artificial and human intelligence come together. Through 23 projects, undergraduates from Barnard and Columbia tackled real-world challenges with creative solutions. From automating accessibility design to redefining how we interact with AI, their work provided a window into the future of technology—and humanity’s place within it. Here we recognize the four award-winning projects selected by six faculty judges.

The November 21 event, presented by the Data Science Institute’s Center for Data, Media and Society, was organized by a group of eight students, and drew a crowd of more than 100.

Award-Winning Projects

Best in Track: Ideas for the Future

Cloudlands – New Architectures and Landscapes of AI

Catherine Mok, Barnard College Class of 2025

This project explores the impact of data centers, now occupying over 100 million square feet in the U.S., on architectural and urban development. Through an interactive exhibit of tactile and virtual models, Catherine Mok (pictured above) traced the evolution of these structures from early server farms to futuristic, eco-friendly prototypes, envisioning how the built environment could adapt to the digital age.

Best in Track: Foundations and Innovations in Technology

MASIVE: Open-Ended Affective State Identification in English and Spanish

Ivan Perez Mejia, Columbia Engineering Class of 2025

By developing MASIVE, a dataset of over 1,000 affective states in English and Spanish, Ivan Perez Mejia pushed the limits of natural language processing, exploring its capacity for recognizing emotion. His findings showed that smaller, fine-tuned multilingual models outperform larger AI systems on region-specific tasks, offering new possibilities for nuanced, culturally aware sentiment analysis.

Best in Track: Interdisciplinary Applications

Procedurally Generated Accessibility Infrastructure

Antonio Sitong Li, Columbia College Class of 2028

The task of designing accessibility ramps was reimagined by Antonio Siting Li, who used an algorithm to automate the design. By using A* pathfinding, the tool calculates optimal paths, balancing slope, height, and obstacles, while producing customizable 3D models that meet accessibility standards and regulations.

Best Demonstration

VHard: An XR UI for Kinesthetic Rehearsal of Rock Climbing Moves

Jace Li, Columbia General Studies Class of 2025

This innovative project explores how VR can enhance rock climbing performance through VHard, an XR tool designed to help climbers approach new routes more efficiently. VHard uses a novel shader linked to a hand-tracking system that provides real-time feedback to help users perfect their climbing moves. Built in Unity and tested on Meta Quest 3 headsets, the tool could open new possibilities in human-computer interaction.

Thanks to the Student Organizers!

Full Organizing Committee: Audrey Acken, Columbia Engineering Class of 2026; Ann Chengying Li, Columbia Engineering Class of 2027; Alexa Kafka, Barnard College Class of 2027; Christine Li, Columbia Engineering Class of 2026; Daniel Alejandro Manjarrez, Columbia Engineering Class of 2026; Jaeyi Song, Columbia Engineering Class of 2028; Shayan Chowdhury, General Studies Class of 2026; Sunny Fang, Barnard College Class of 2025.

From Left to Right: Sunny Fang; Christine Li; Daniel Alejandro Manjarrez; Alexa Kafka; Ann Chengying Li; Shayan Chowdhury

This annual research fair is produced by the Data Science Institute’s Center for Data, Media and Society, co-chaired by Susan McGregor, Data Science Institute Research Scholar, and Eugene Wu, Associate Professor of Computer Science at Columbia Engineering.