Ranjitha Kumar
Ranjitha Kumar
ranju [at] stanford [dot] edu  ·  cv
I'm a PhD student in the Department of Computer Science at Stanford University, working with Scott Klemmer. My research focuses on leveraging machine learning techniques to build tools for amplifying human creativity.
papers
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Flexible Tree Matching
Ranjitha Kumar, Jerry O. Talton, Salman Ahmad, Tim Roughgarden, Scott R. Klemmer
Proceedings of IJCAI '11
 ·  invited paper
Tree-matching problems arise in many computational domains. The literature provides several methods for creating correspondences between labeled trees; however, by definition, tree-matching algorithms rigidly preserve ancestry. That is, once two nodes have been placed in correspondence, their descendants must be matched as well. We introduce flexible tree matching, which relaxes this rigid requirement in favor of a tunable formulation in which the role of hierarchy can be controlled. We show that flexible tree matching is strongly NP-complete, give a stochastic approximation algorithm for the problem, and demonstrate how structured prediction techniques can learn the algorithm's parameters from a set of example matchings. Finally, we present results from applying the method to tasks in Web design.
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Bricolage: Example-Based Retargeting for Web Design
Ranjitha Kumar, Jerry O. Talton, Salman Ahmad, Scott R. Klemmer
Proceedings of CHI '11
 ·  www ·  best paper award
The Web provides a corpus of design examples unparalleled in human history. However, leveraging existing designs to produce new pages is often difficult. This paper introduces the Bricolage algorithm for transferring design and content between Web pages. Bricolage employs a novel, structured-prediction technique that learns to create coherent mappings between pages by training on human-generated exemplars. The produced mappings are then used to automatically transfer the content from one page into the style and layout of another. We show that Bricolage can learn to accurately reproduce human page mappings, and that it provides a general, efficient, and automatic technique for retargeting content between a variety of real Web pages.
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Designing with Interactive Example Galleries
Brian Lee, Savil Srivastava, Ranjitha Kumar, Ronen Brafman, Scott R Klemmer
Proceedings of CHI '10
Designers often use examples for inspiration; examples offer contextualized instances of how form and content integrate. Can interactive example galleries bring this practice to everyday users doing design work, and does working with examples help the designs they create? This paper explores whether people can realize significant value from explicit mechanisms for designing by example modification. We present the results of three studies, finding that independent raters prefer designs created with the aid of examples, that users prefer adaptively selected examples to random ones, and that users make use of multiple examples when creating new designs. To enable these studies and demonstrate how software tools can facilitate designing with examples, we introduce interface techniques for browsing and borrowing from a corpus of examples, manifest in the Adaptive Ideas Web design tool. Adaptive Ideas leverages a faceted metadata interface for viewing and navigating example galleries.
posters
UIST Poster '09 Thumbnail
Crowdsourcing Interface for Collecting Correspondences Between Web Pages
Juho Kim, Ranjitha Kumar, and Scott R Klemmer
Poster, Adjunct Proceedings of UIST '09
CHI Poster '09 Thumbnail
Automatic Retargeting of Web Page Content
Ranjitha Kumar, Juho Kim, and Scott R Klemmer
Poster, Extended Abstracts of CHI '09
tech reports
skeleton muscle front  skeleton muscle back
Volume Preserving Sinusoidal Muscles for Surface Skinning
Ranjitha Kumar
Senior Honors Thesis, Stanford University '07
This thesis presents a volume-preserving analytic muscle model that can be embedded within a skin mesh to induce realistic, physics-based deformation during simulation. These volumetric muscles are layered on top of a dynamic framework of linear, segment-based muscles that drive the underlying skeletal structure. The result is an integrated system that supports realistic skin deformation along a specified target motion while requiring only minimal computational resources.
honors and awards
Google PhD Fellowship (2011)
ACM CHI Best Paper Award (2011)
NSF Graduate Research Fellowship Competition, Honorable Mention (2007, 2008)
Stanford University School of Engineering Fellowship (2007)
Computer Research Association Outstanding Undergraduate, Honorable Mention (2007)
teaching
Course Assistant, Stanford University (Fall 2008)
CS147: Introduction to HCI Design
Stanford Teaching Fellow, Stanford University (Summer 2008)
CS148: Introductory Computer Graphics
Course Assistant, Stanford University (Summer 2007)
CS148: Introductory Computer Graphics
Instructor, Stanford University (Fall 2006)
CS1C: Introduction to Computing at Stanford