Best Research Paper and Best Student Research Paper, KDD'17
Early Career Award
We study the influence of the language of cartoon captions on the perceived humorousness of the cartoons. Our studies are based on a large corpus of crowdsourced cartoon captions that were submitted to a contest hosted by the New Yorker.
Also became a book chapter: The Languages of Humor: Verbal, Visual, and Physical Humor, Bloomsbury Academic, 2018
MIT Tech Review: This week's most thought-provoking papers from the Physics arXiv. (Link)
We propose a methodology for creating structured summaries of information, which we call zoomable metro maps. Just as cartographic maps have been relied upon for centuries to help us understand our surroundings, metro maps can help us understand the information landscape. [...] As different users might be interested in different levels of granularity, the maps are zoomable, with each level of zoom showing finer details and interactions.
Complex stories spaghetti into branches, side stories, and intertwining narratives. In order to explore these stories, one needs a map to navigate unfamiliar territory. We propose a methodology for creating structured summaries of information, which we call metro maps. [...] Most importantly, metro maps explicitly show the relations among retrieved pieces in a way that captures story development.
Best Research Paper, KDD'10
In recent years, the blogosphere has experienced a substantial increase in the number of posts published daily, forcing users to cope with information overload. [...] we present a principled approach for picking a set of posts that best covers the important stories in the blogosphere. [...] In addition, since people have varied interests, our coverage algorithm incorporates user preferences in order to tailor the selected posts to individual tastes.
We discuss challenges and opportunities for developing generalized task markets where human and machine intelligence are enlisted to solve problems, based on a consideration of the competencies, availabilities, and pricing of different problemsolving resources. The approach couples human computation with machine learning and planning, and is aimed at optimizing the flow of subtasks to people and to computational problem solvers. We illustrate key ideas in the context of Lingua Mechanica, a project focused on harnessing human and machine translation skills to perform translation among languages.
3rd most-cited paper of AAAI'10 (@2014)
(A thought experiment: complexity models for computational problems that include a human in the process. Take with a grain of salt)