Publication statistics

Pub. period:2006-2010
Pub. count:8
Number of co-authors:16



Co-authors

Number of publications with 3 favourite co-authors:

James Fogarty:5
James A. Landay:5
T. Scott Saponas:2

 

 

Productive colleagues

Kayur Patel's 3 most productive colleagues in number of publications:

James A. Landay:91
Jacob O. Wobbrock:71
James Fogarty:35
 
 
 
Jul 23

Men have become the tools of their tools.

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Kayur Patel

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Publications by Kayur Patel (bibliography)

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2010
 
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Patel, Kayur (2010): Lowering the barrier to applying machine learning. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 2907-2910.

Researchers have used machine learning algorithms to solve hard problems in a variety of domains, enabling exciting, new applications of computing. However, research results have not transferred to software solutions. In part, this is because developing software with machine learning algorithms is itself difficult. My dissertation work aims to understand why using machine learning is difficult and to create tools that lower the bar so that more developers can effectively use machine learning.

© All rights reserved Patel and/or his/her publisher

 
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Patel, Kayur, Bancroft, Naomi, Drucker, Steven M., Fogarty, James, Ko, Andrew J. and Landay, James A. (2010): Gestalt: integrated support for implementation and analysis in machine learning. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 37-46.

We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we explicitly support both code and data. Gestalt allows developers to implement a classification pipeline, analyze data as it moves through that pipeline, and easily transition between implementation and analysis. An experiment shows this significantly improves the ability of developers to find and fix bugs in machine learning systems. Our discussion of Gestalt and our experimental observations provide new insight into general-purpose support for the machine learning process.

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

 
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Patel, Kayur (2010): Lowering the barrier to applying machine learning. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 355-358.

Machine learning algorithms are key components in many cutting edge applications of computation. However, the full potential of machine learning has not been realized because using machine learning is hard, even for otherwise tech-savvy developers. This is because developing with machine learning is different than normal programming. My thesis is that developers applying machine learning need new general-purpose tools that provide structure for common processes and common pipelines while remaining flexible to account for variability in problems. In this paper, I describe my efforts to understanding the difficulties that developers face when applying machine learning. I then describe Gestalt, a general-purpose integrated development environment designed the application of machine learning. Finally, I describe work on developing a pattern language for building machine learning systems and creating new techniques that help developers understand the interaction between their data and learning algorithms.

© All rights reserved Patel and/or his/her publisher

2009
 
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Hoffmann, Raphael, Amershi, Saleema, Patel, Kayur, Wu, Fei, Fogarty, James and Weld, Daniel S. (2009): Amplifying community content creation with mixed initiative information extraction. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 1849-1858.

Although existing work has explored both information extraction and community content creation, most research has focused on them in isolation. In contrast, we see the greatest leverage in the synergistic pairing of these methods as two interlocking feedback cycles. This paper explores the potential synergy promised if these cycles can be made to accelerate each other by exploiting the same edits to advance both community content creation and learning-based information extraction. We examine our proposed synergy in the context of Wikipedia infoboxes and the Kylin information extraction system. After developing and refining a set of interfaces to present the verification of Kylin extractions as a non primary task in the context of Wikipedia articles, we develop an innovative use of Web search advertising services to study people engaged in some other primary task. We demonstrate our proposed synergy by analyzing our deployment from two complementary perspectives: (1) we show we accelerate community content creation by using Kylin's information extraction to significantly increase the likelihood that a person visiting a Wikipedia article as a part of some other primary task will spontaneously choose to help improve the article's infobox, and (2) we show we accelerate information extraction by using contributions collected from people interacting with our designs to significantly improve Kylin's extraction performance.

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

2008
 
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Patel, Kayur, Fogarty, James, Landay, James A. and Harrison, Beverly L. (2008): Investigating statistical machine learning as a tool for software development. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 667-676.

As statistical machine learning algorithms and techniques continue to mature, many researchers and developers see statistical machine learning not only as a topic of expert study, but also as a tool for software development. Extensive prior work has studied software development, but little prior work has studied software developers applying statistical machine learning. This paper presents interviews of eleven researchers experienced in applying statistical machine learning algorithms and techniques to human-computer interaction problems, as well as a study of ten participants working during a five-hour study to apply statistical machine learning algorithms and techniques to a realistic problem. We distill three related categories of difficulties that arise in applying statistical machine learning as a tool for software development: (1) difficulty pursuing statistical machine learning as an iterative and exploratory process, (2) difficulty understanding relationships between data and the behavior of statistical machine learning algorithms, and (3) difficulty evaluating the performance of statistical machine learning algorithms and techniques in the context of applications. This paper provides important new insight into these difficulties and the need for development tools that better support the application of statistical machine learning.

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

 
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Harada, Susumu, Lester, Jonathan, Patel, Kayur, Saponas, T. Scott, Fogarty, James, Landay, James A. and Wobbrock, Jacob O. (2008): VoiceLabel: using speech to label mobile sensor data. In: Digalakis, Vassilios, Potamianos, Alexandros, Turk, Matthew, Pieraccini, Roberto and Ivanov, Yuri (eds.) Proceedings of the 10th International Conference on Multimodal Interfaces - ICMI 2008 October 20-22, 2008, Chania, Crete, Greece. pp. 69-76.

 
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Harada, Susumu, Lester, Jonathan, Patel, Kayur, Saponas, T. Scott, Fogarty, James, Landay, James A. and Wobbrock, Jacob O. (2008): VoiceLabel: using speech to label mobile sensor data. In: Proceedings of the 2008 International Conference on Multimodal Interfaces 2008. pp. 69-76.

Many mobile machine learning applications require collecting and labeling data, and a traditional GUI on a mobile device may not be an appropriate or viable method for this task. This paper presents an alternative approach to mobile labeling of sensor data called VoiceLabel. VoiceLabel consists of two components: (1) a speech-based data collection tool for mobile devices, and (2) a desktop tool for offline segmentation of recorded data and recognition of spoken labels. The desktop tool automatically analyzes the audio stream to find and recognize spoken labels, and then presents a multimodal interface for reviewing and correcting data labels using a combination of the audio stream, the system's analysis of that audio, and the corresponding mobile sensor data. A study with ten participants showed that VoiceLabel is a viable method for labeling mobile sensor data. VoiceLabel also illustrates several key features that inform the design of other data labeling tools.

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

2006
 
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Patel, Kayur, Chen, Mike Y., Smith, Ian and Landay, James A. (2006): Personalizing routes. In: Proceedings of the ACM Symposium on User Interface Software and Technology 2006. pp. 187-190.

Navigation services (e.g., in-car navigation systems and online mapping sites) compute routes between two locations to help users navigate. However, these routes may direct users along an unfamiliar path when a familiar path exists, or, conversely, may include redundant information that the user already knows. These overly complicated directions increase the cognitive load of the user, which may lead to a dangerous driving environment. Since the level of detail is user specific and depends on their familiarity with a region, routes need to be personalized. We have developed a system, called MyRoute, that reduces route complexity by creating user specific routes based on a priori knowledge of familiar routes and landmarks. MyRoute works by compressing well known steps into a single contextualized step and rerouting users along familiar routes.

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

 
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Changes to this page (author)

20 Apr 2011: Modified
03 Nov 2010: Modified
03 Nov 2010: Modified
02 Nov 2010: Modified
30 May 2009: Modified
09 May 2009: Modified
12 May 2008: Modified
12 May 2008: Modified
24 Jul 2007: Added

Page Information

Page maintainer: The Editorial Team
URL: http://www.interaction-design.org/references/authors/kayur_patel.html

Publication statistics

Pub. period:2006-2010
Pub. count:8
Number of co-authors:16



Co-authors

Number of publications with 3 favourite co-authors:

James Fogarty:5
James A. Landay:5
T. Scott Saponas:2

 

 

Productive colleagues

Kayur Patel's 3 most productive colleagues in number of publications:

James A. Landay:91
Jacob O. Wobbrock:71
James Fogarty:35
 
 
 
Jul 23

Men have become the tools of their tools.

-- Henry David Thoreau

 
 

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

Kumar and Herger 2013: Gamification at Work: Designing Engaging Business Software...
by Janaki Mythily Kumar and Mario Herger

 
Start reading

Whitworth and Ahmad 2013: The Social Design of Technical Systems: Building technologies for communities...
by Brian Whitworth and Adnan Ahmad

 
Start reading

Soegaard and Dam 2013: The Encyclopedia of Human-Computer Interaction, 2nd Ed....
by Mads Soegaard and Rikke Friis Dam

 
Start reading
 
 

Help us help you!