Bio
I am currently a PhD student in the Carnegie Mellon Human Computer Interaction Institute (HCII) working under Niki Kittur. My current research focuses on the topic of Sensemaking — or how individuals come to an understanding of a difficult subject from a large set of information. This is normally in the context of online search or information seeking — such as planning a large vacation, learning about a medical disease, or investigating a new hobby. In our lab, we've built systems to help users while they're sensemaking, using techniques such as crowdsourcing, visualization, and natural language processing.
Selected Work
Casual Microtasking
Nathan Hahn, Shamsi Iqbal, Jaime Teevan
We've been working on the concept of "casual microtasking", or giving people small tasks to work on while they're doing other activities. In this paper, we explored this with Facebook -- we inserted small writing tasks into users' Facebook news feed, and gathered some responses about their experience with it.
SearchLens
Joseph Chang, Nathan Hahn, Adam Peerer, Niki Kittur
SearchLens is a replacement search interface for exploratory searches, where users might have multiple categories or criteria they need to fulfil. By building up "lenses", users can easily structure what they want into a query, assign weights, and then see how results matched their preferences.
Bento Browser
Nathan Hahn, Joseph Chang, Niki Kittur
Bento Browser is a prototype iOS application that helps people organize their complex online searching tasks. By removing the typical tab based structure of the browser and replacing it with a task hierarchy, we let people seamlessly organize, suspend and resume their searching tasks. Check it out on the App Store!
Knowledge Accelerator (KA)
Nathan Hahn, Joseph Chang, Ji Eun Kim, Niki Kittur
The Knowledge Accelerator (KA) uses crowdworkers to synthesize different information sources on the web in response to a query. We prototyped this system in order to explore crowdsourcing complex, high context tasks in a microtask enviornment. Our system performed quite well, checkout the answers it produced!