Publication statistics

Pub. period:2007-2011
Pub. count:5
Number of co-authors:10


Number of publications with 3 favourite co-authors:

Ed Chi:
Manolis Savva:
Arti Chhajta:



Productive colleagues

Nicholas Kong's 3 most productive colleagues in number of publications:

Ravin Balakrishnan:108
Tovi Grossman:44
Maneesh Agrawala:36

Upcoming Courses

go to course
Gestalt Psychology and Web Design: The Ultimate Guide
Starts the day after tomorrow !
go to course
Become a UX Designer from scratch
92% booked. Starts in 3 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

Nicholas Kong


Publications by Nicholas Kong (bibliography)

 what's this?
Edit | Del

Kong, Nicholas, Convertino, Gregorio, Hanrahan, Benjamin and Chi, Ed (2011): VisualWikiCurator: a corporate Wiki plugin. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 1549-1554.

Knowledge workers who maintain corporate wikis face high costs for organizing and updating content on wikis. This problem leads to low adoption rates and compromises the utility of such tools in organizations. We describe a system that seeks to reduce the interactions costs of updating and organizing wiki pages by combining human and machine intelligence. We then present preliminary results of an ongoing evaluation of the tool.

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

Edit | Del

Savva, Manolis, Kong, Nicholas, Chhajta, Arti, Fei-Fei, Li, Agrawala, Maneesh and Heer, Jeffrey (2011): ReVision: automated classification, analysis and redesign of chart images. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 393-402.

Poorly designed charts are prevalent in reports, magazines, books and on the Web. Most of these charts are only available as bitmap images; without access to the underlying data it is prohibitively difficult for viewers to create more effective visual representations. In response we present ReVision, a system that automatically redesigns visualizations to improve graphical perception. Given a bitmap image of a chart as input, ReVision applies computer vision and machine learning techniques to identify the chart type (e.g., pie chart, bar chart, scatterplot, etc.). It then extracts the graphical marks and infers the underlying data. Using a corpus of images drawn from the web, ReVision achieves image classification accuracy of 96% across ten chart categories. It also accurately extracts marks from 79% of bar charts and 62% of pie charts, and from these charts it successfully extracts data from 71% of bar charts and 64% of pie charts. ReVision then applies perceptually-based design principles to populate an interactive gallery of redesigned charts. With this interface, users can view alternative chart designs and retarget content to different visual styles.

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

Edit | Del

Heer, Jeffrey, Kong, Nicholas and Agrawala, Maneesh (2009): Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 1303-1312.

We investigate techniques for visualizing time series data and evaluate their effect in value comparison tasks. We compare line charts with horizon graphs -- a space-efficient time series visualization technique -- across a range of chart sizes, measuring the speed and accuracy of subjects' estimates of value differences between charts. We identify transition points at which reducing the chart height results in significantly differing drops in estimation accuracy across the compared chart types, and we find optimal positions in the speed-accuracy tradeoff curve at which viewers performed quickly without attendant drops in accuracy. Based on these results, we propose approaches for increasing data density that optimize graphical perception.

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

Edit | Del

Kong, Nicholas and Agrawala, Maneesh (2009): Perceptual interpretation of ink annotations on line charts. In: Proceedings of the ACM Symposium on User Interface Software and Technology 2009. pp. 233-236.

Asynchronous collaborators often use freeform ink annotations to point to visually salient perceptual features of line charts such as peaks or humps, valleys, rising slopes and declining slopes. We present a set of techniques for interpreting such annotations to algorithmically identify the corresponding perceptual parts. Our approach is to first apply a parts-based segmentation algorithm that identifies the visually salient perceptual parts in the chart. Our system then analyzes the freeform annotations to infer the corresponding peaks, valleys or sloping segments. Once the system has identified the perceptual parts it can highlight them to draw further attention and reduce ambiguity of interpretation in asynchronous collaborative discussions.

© All rights reserved Kong and Agrawala and/or their publisher

Edit | Del

Grossman, Tovi, Kong, Nicholas and Balakrishnan, Ravin (2007): Modeling pointing at targets of arbitrary shapes. In: Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems 2007. pp. 463-472.

We investigate pointing at graphical targets of arbitrary shapes. We first describe a previously proposed probabilistic Fitts' law model [7] which, unlike previous models that only account for rectangular targets, has the potential to handle arbitrary shapes. Three methods of defining the centers of arbitrarily shaped targets for use within the model are developed. We compare these methods of defining target centers, and validate the model using a pointing experiment in which the targets take on various shapes. Results show that the model can accurately account for the varying target shapes. We discuss the implications of our results to interface design.

© All rights reserved Grossman 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