Publication statistics

Pub. period:2009-2012
Pub. count:5
Number of co-authors:12


Number of publications with 3 favourite co-authors:

Lingfeng Yang:
Jerry Talton:
Maxine Lim:



Productive colleagues

Ranjitha Kumar's 3 most productive colleagues in number of publications:

Scott R. Klemmer:38
Brian Lee:5
Juho Kim:4

Upcoming Courses

go to course
Get Your First Job as a UX or Interaction Designer
go to course
Information Visualization: Getting Dashboards Right
90% booked. Starts in 5 days

Featured chapter

Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess

User Experience and Experience Design !


Our Latest Books

The Glossary of Human Computer Interaction
by Mads Soegaard and Rikke Friis Dam
start reading
The Social Design of Technical Systems: Building technologies for communities. 2nd Edition
by Brian Whitworth and Adnan Ahmad
start reading
Gamification at Work: Designing Engaging Business Software
by Janaki Mythily Kumar and Mario Herger
start reading
The Social Design of Technical Systems: Building technologies for communities
by Brian Whitworth and Adnan Ahmad
start reading
The Encyclopedia of Human-Computer Interaction, 2nd Ed.
by Mads Soegaard and Rikke Friis Dam
start reading

Ranjitha Kumar


Publications by Ranjitha Kumar (bibliography)

 what's this?
Edit | Del

Talton, Jerry, Yang, Lingfeng, Kumar, Ranjitha, Lim, Maxine, Goodman, Noah and Mech, Radomr (2012): Learning design patterns with Bayesian grammar induction. In: Proceedings of the 2012 ACM Symposium on User Interface Software and Technology 2012. pp. 63-74.

Design patterns have proven useful in many creative fields, providing content creators with archetypal, reusable guidelines to leverage in projects. Creating such patterns, however, is a time-consuming, manual process, typically relegated to a few experts in any given domain. In this paper, we describe an algorithmic method for learning design patterns directly from data using techniques from natural language processing and structured concept learning. Given a set of labeled, hierarchical designs as input, we induce a probabilistic formal grammar over these exemplars. Once learned, this grammar encodes a set of generative rules for the class of designs, which can be sampled to synthesize novel artifacts. We demonstrate the method on geometric models and Web pages, and discuss how the learned patterns can drive new interaction mechanisms for content creators.

© All rights reserved Talton et al. and/or ACM Press

Edit | Del

Kumar, Ranjitha (2012): Data-driven interactions for web design. In: Adjunct Proceedings of the 2012 ACM Symposium on User Interface Software and Technology 2012. pp. 51-54.

This thesis describes how data-driven approaches to Web design problems can enable useful interactions for designers. It presents three machine learning applications which enable new interaction mechanisms for Web design: rapid retargeting between page designs, scalable design search, and generative probabilistic model induction to support design interactions cast as probabilistic inference. It also presents a scalable architecture for efficient data-mining on Web designs, which supports these three applications.

© All rights reserved Kumar and/or ACM Press

Edit | Del

Kumar, Ranjitha, Talton, Jerry O., Ahmad, Salman and Klemmer, Scott R. (2011): Bricolage: example-based retargeting for web design. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 2197-2206.

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.

© All rights reserved Kumar et al. and/or their publisher

Edit | Del

Lee, Brian, Srivastava, Savil, Kumar, Ranjitha, Brafman, Ronen and Klemmer, Scott R. (2010): Designing with interactive example galleries. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 2257-2266.

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 examples may benefit novices more than experienced designers, 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.

© All rights reserved Lee et al. and/or their publisher

Edit | Del

Kumar, Ranjitha, Kim, Juho and Klemmer, Scott R. (2009): Automatic retargeting of web page content. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 4237-4242.

We present a novel technique for automatically retargeting content from one web page onto the layout of another. Web pages are decomposed into their perceptual hierarchical representations. We then use a structured-prediction algorithm to learn reasonable mappings between the perceptual trees. Using the mappings, we are able to merge the content of one page with the layout of another.

© All rights reserved Kumar et al. and/or ACM Press

Add publication
Show list on your website

Join our community and advance:




Join our community!

Page Information

Page maintainer: The Editorial Team