On-the-go Crowdsourcing

On-the-Go Crowdsourcing studies the design of interactions, algorithms and architectures that conveniently leverage people’s existing mobility and routines for crowdsourcing and self-sourcing. Projects center around three core areas: (1) enabling large-scale, high-fidelity communitysensing through lightweight interactions; (2) empowering community-supported physical tasking through people’s existing routines; (3) using location-aware reminders to promote completing tasks at home for oneself. Our research aims to enable physical crowdsourcing systems to tap into the rich daily physical routines of large numbers of people to better transport goods, map the world in exquisite new detail, and accomplish a broad range of tasks at scale. Our work will lead to a general framework and set of techniques that aim to achieve this by (1) scaffolding individual contributions toward a communal goal, (2) developing computational models and mechanisms that flexibly guide people to appropriate tasks and intelligently manage community participation.


Yongsung Kim
Aaron Loh
Emily Harburg
Nicole Zhu
Shana Azria
Stephen Chan
Zak Allen
Kapil Garg
Sasha Weiss
Haoqi Zhang