Meaning Augmenting Art with Technology, Art++ aims to improve the experience of visitors in a museum gallery by proposing a new way of delivering information to them. Using augmented reality, Art++ will offer viewers an immersive and interactive learning experience by overlaying content directly on the objects through the viewfinder of a smartphone or tablet device. The Art++ team consists of Jean-Baptiste Boin, a PhD candidate in electrical engineering at Stanford University, and Colleen Stockmann, assistant curator for special projects at the Cantor Arts Center at Stanford University.
Cannabis Wire will create a highly visual and interactive data driven single-subject news site aiming to simplify the complexities of cannabis legalization and its role in the broader drug war and criminal justice system. The Cannabis Wire team consists of Alyson Martin and Nushin Rashidian, alumnae of the Graduate School of Journalism at Columbia University and co-authors of A New Leaf: The End of Cannabis Prohibition. Project web site.
In direct response to criticisms of the rigor of business journalism, Earnings Inspector will provide business journalists a new tool to make the methods of forensic accounting more accessible. By sifting through a database of accounts of all public U.S. companies, Earnings Inspector will use fraud detection algorithms to report the likelihood of manipulated earnings. The Earnings Inspector team consists of Caelainn Barr, Cecile Schilis-Gallego and Daniel Drepper, students at the Graduate School of Journalism at Columbia University.
Journalists can glean remarkable insights into the social and cultural tensions of a region by studying the lives and experiences of its artists. These insights are particularly important in countries whose cultures have been misconstrued by traditional reporting in mainstream media. Built on this notion, Reframe Iran will present 40 profiles of Iranian artists living both in Iran and abroad, using text, photo, and the innovative medium of immersive video. The Refame Iran team consists of Matt Yu, PhD candidate in electrical engineering at Stanford University, and Alexandra Glorioso, Joao Inada and Matteo Lonardi, students at the Graduate School of Journalism at Columbia University.
One of the biggest challenges facing science journalists is the ability to quickly contextualize journal articles they are reporting on deadline. Science Surveyor is a tool that can help science journalists and others rapidly and effectively characterize the scientific literature for any topic by providing a contextual consensus, a timeline of publications surrounding the topic, and categorized funding. The Science Surveyor team consists of Marguerite Holloway, director of science and environmental journalism and assistant professor at the Graduate School of Journalism at Columbia University; Laura Kurgan, director of the Spatial Information Design Lab at the Graduate School of Architecture, Preservation and Planning at Columbia University, and Juan Francisco Saldarriaga, associate research scholar and adjunct assistant professor of urban planning at the Graduate School of Architecture, Preservation and Planning at Columbia University. The Science Surveyor proposal also includes representation from Stanford – Laura Moorhead and Cheryl Holzmeyer, a Ph.D. candidate and a postdoctoral research fellow, respectively, at the Graduate School of Education, and Juan Pablo Alperin, a Ph.D. candidate with the Public Knowledge Project.
Visual Genome seeks to enable journalists to effectively gather crowd-sourced breaking news images and videos in near real time, extract meta-data and relationships from these images, and utilize them to enhance the quality of their articles and reports. The Visual Genome team consists of Ranjay Krishna, a graduate student in computer science at Stanford Engineering, and Justin Johnson, a PhD candidate in computer science at Stanford Engineering.
By revealing the mechanisms behind previously opaque advertising schemes at an arbitrarily detailed level, the Web Transparency project unveils today’s commercial and political tactics that are used to funnel consumers and citizens’ attention. The Web Transparency team consists of Charles Berret, a PhD candidate in communications at Columbia University, Cecilia Reyes, an undergraduate in computer science at Columbia University, and Max Tucker, a software developer at the Institute for Software Research.
Widescope and Synapp, a 2013-14 Magic Grant recipient, will receive renewed funding to scale up the current systems to achieve widespread usage and impact by partnering with governments, schools, and media organizations. Additionally, the team will further develop and implement algorithms and mechanisms for more effective aggregation and collaboration, all in an effort to posit online social media as an enabler of deliberative and participatory democracy. The Widescope and Synapp team consists of David Lee, a PhD candidate in electrical engineering at Stanford Engineering, and Sukolsak Sakshuwong, a graduate student in computer science at Stanford Engineering.
A Columbia School of Journalism documentary film student and a PhD candidate in the Rutgers University School of Communication and Information will tell the story of a drag renaissance taking place in Bushwick, Brooklyn, that is enlisting and extending social media platforms for the “identity curation” that happens in the drag community.
A collaboration between The New York World housed in Columbia Journalism School, and the Social Media Information Lab at Rutgers University, this project will look for newsworthy events in the patterns of real-time, geotagged social media feeds. (Half of this project’s budget will come from the Tow Center.)
Ensemble is a Web platform created by Joy Kim and Justin heng that provides structure to collaborative storytelling. In Ensemble, one person is assigned the responsibility of managing creative direction, and can then enlist a crowd of friends or strangers to perform various tasks – such as contributing narrative direction or developing a character's back story – with an ultimate goal of creating more engaging stories by drawing from the different personal viewpoints and experiences of a group.
A partnership between faculty and students in the Departments of History, Statistics and Computer Science at Columbia University, this project will probe the limits of official secrecy by applying natural language processing software to archives of declassified documents to examine whether it is possible to predict the contents of redacted text, attribute authorship to anonymous documents and model the geographic and temporal patterns of diplomatic communications. (Half of this project’s budget will come from the Tow Center.)
A collaboration by Richard Socher and Rebecca Weiss, Gistraker is a Web application that analyzes the sentiment of language used in news media. Users will be able to create filters and explore visual summaries of how different media outlets cover specific actors or issues of interest, which could reveal instances of media bias.
A team of graduate students and recent graduates of the Columbia School of Journalism and the School of Engineering and Applied Science will create a system for tracking censorship in authoritarian regimes post-publication (i.e. when a story is revised or deleted after publication). The team will create real-time assessments and monthly reports of journalistic improprieties around the globe.
A project proposed by Hao Su, Matt Yu, Roland Angst and Peter Vajda, STAR will experiment with using augmented reality software on mobile devices in combination with location –and viewpoint-aware storytelling. The group hopes to foster more interactive and immersive storytelling by displaying a video stream of virtual content that overlaps with live images of the physical world as viewed on a mobile device.
Team Members: Kanak Biscuitwala and William Bult of Stanford; Mathias Lecuyer and Madeline Ross of Columbia. Faculty Advisors: Prof. Augustin Chaintreau, Prof. Monica Lam, and Prof. Susan McGregor. Industry Partner: Chris Haseman (Tumblr).