I'm a 2nd year Ph.D. student at Northwestern University in the Technology and Social Behavior (TSB) program advised by Professor Haoqi Zhang and Professor Darren Gergle. My research interest broadly falls at the intersection of human-computer interaction (HCI), social and crowd computing, and artificial intelligence. Here, I wish to build human-machine systems that deeply intertwine the abilities of people to complete intricate tasks with the abilities of machines to greatly scale their impact beyond what any individual could do alone.
My current work explores how we can scale authentic undergraduate research training by developing systems that help students identify and address their needs while they self-direct their research work, and help mentors coordinate students to community resources without overburdening the resources.
In my free time, I enjoy: reading, hacking, eating, and making coffee.
Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing trade-offs between providing convenient opportunities for contributors and collecting high-fidelity data. This paper explores a new, hybrid approach, in which collected data–even if low-fidelity initially–can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users’ routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.