We present a crowdsourcing algorithm that enables paid workers to create context-appropriate and tone-sensitive behavior messages. Our approach is the first to leverage crowd-workers for generating “fresh” messages.
Recommending Lifestyle and Behavior Changes -
Modeling people’s context, habits, lifestyles, wellness goals and their achievements and failures allow interventions that are personalized to the needs of the individual. Proposed a recommender system that leverages user-similariy to recommend the most effective lifestyle choices.
Our in-depth analysis of a microlending website shows that lenders appear to lend more to highly rated institutions, and with what appears to be better planned lending decisions; and that smaller, homogeneous teams seem to drive more lending activity and to achieve larger team lending agreements.
Twitter Quakes -
Developed a technique to measure the impact of global events on different geographic locations. Our technique measures significant increase in the volume of tweets during specific time periods, which are generally caused by some significant happening of the event.
- [07/02/14] Had an excellent time at HCIC’14
- [05/04/14] Glad to be a CHI’14 SV
- [05/04/14] CHI’14 Workshop was very useful
- [02/17/14] CHI’14 Workshop paper accepted
- [02/15/14] CSCW’14 SVing was a lot of fun
- [02/01/14] CHI’14 looking forward to SV
- [01/15/14] HCIC’14 Web Developer