Publication statistics

Pub. period:2007-2011
Pub. count:10
Number of co-authors:18


Number of publications with 3 favourite co-authors:

Neema Moraveji:
Desney Tan:
Ashish Kapoor:



Productive colleagues

Saleema Amershi's 3 most productive colleagues in number of publications:

Ravin Balakrishnan:108
Meredith Ringel Mo..:38
James Fogarty:35

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Saleema Amershi


Publications by Saleema Amershi (bibliography)

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Amershi, Saleema, Lee, Bongshin, Kapoor, Ashish, Mahajan, Ratul and Christian, Blaine (2011): CueT: human-guided fast and accurate network alarm triage. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 157-166.

Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a-priori and evolve constantly. A user study with real operators and data from a large network shows that CueT significantly improves the speed and accuracy of alarm triage compared to the network's current practice.

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Amershi, Saleema (2011): Designing for effective end-user interaction with machine learning. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 47-50.

End-user interactive machine learning is a promising tool for enhancing human capabilities with large data. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. My dissertation work aims to advance our understanding of this question by investigating new techniques that move beyond nave or ad-hoc approaches and balance the needs of both end-users and machine learning algorithms. Although these explorations are grounded in specific applications, we endeavored to design strategies independent of application or domain specific features. As a result, our findings can inform future end-user interaction with machine learning systems.

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Amershi, Saleema, Fogarty, James, Kapoor, Ashish and Tan, Desney (2010): Examining multiple potential models in end-user interactive concept learning. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 1357-1360.

End-user interactive concept learning is a technique for interacting with large unstructured datasets, requiring insights from both human-computer interaction and machine learning. This note re-examines an assumption implicit in prior interactive machine learning research, that interaction should focus on the question "what class is this object?". We broaden interaction to include examination of multiple potential models while training a machine learning system. We evaluate this approach and find that people naturally adopt revision in the interactive machine learning process and that this improves the quality of their resulting models for difficult concepts.

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Amershi, Saleema, Morris, Meredith Ringel, Moraveji, Neema, Balakrishnan, Ravin and Toyama, Kentaro (2010): Multiple mouse text entry for single-display groupware. In: Proceedings of ACM CSCW10 Conference on Computer-Supported Cooperative Work 2010. pp. 169-178.

A recent trend in interface design for classrooms in developing regions has many students interacting on the same display using mice. Text entry has emerged as an important problem preventing such mouse-based single-display groupware systems from offering compelling interactive activities. We explore the design space of mouse-based text entry and develop 13 techniques with novel characteristics suited to the multiple mouse scenario. We evaluated these in a 3-phase study over 14 days with 40 students in 2 developing region schools. The results show that one technique effectively balanced all of our design dimensions, another was most preferred by students, and both could benefit from augmentation to support collaborative interaction. Our results also provide insights into the factors that create an optimal text entry technique for single-display groupware systems.

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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.

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Amershi, Saleema and Morris, Meredith Ringel (2009): Co-located collaborative web search: understanding status quo practices. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 3637-3642.

Co-located collaborative Web search is a surprisingly common activity, despite the fact that Web browsers and search engines are not designed to support collaboration. We report the findings of two studies (a diary study and an observational study) that provide insights regarding the frequency of co-located collaborative searching, the strategies participants use, and the pros and cons of these strategies. We then articulate design implications for next-generation tools that could enhance the experience of co-located collaborative search.

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Amershi, Saleema, Fogarty, James, Kapoor, Ashish and Tan, Desney (2009): Overview based example selection in end user interactive concept learning. In: Proceedings of the ACM Symposium on User Interface Software and Technology 2009. pp. 247-256.

Interaction with large unstructured datasets is difficult because existing approaches, such as keyword search, are not always suited to describing concepts corresponding to the distinctions people want to make within datasets. One possible solution is to allow end users to train machine learning systems to identify desired concepts, a strategy known as interactive concept learning. A fundamental challenge is to design systems that preserve end user flexibility and control while also guiding them to provide examples that allow the machine learning system to effectively learn the desired concept. This paper presents our design and evaluation of four new overview based approaches to guiding example selection. We situate our explorations within CueFlik, a system examining end user interactive concept learning in Web image search. Our evaluation shows our approaches not only guide end users to select better training examples than the best performing previous design for this application, but also reduce the impact of not knowing when to stop training the system. We discuss challenges for end user interactive concept learning systems and identify opportunities for future research on the effective design of such systems.

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Amershi, Saleema and Morris, Meredith Ringel (2008): CoSearch: a system for co-located collaborative web search. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 1647-1656.

Web search is often viewed as a solitary task; however, there are many situations in which groups of people gather around a single computer to jointly search for information online. We present the findings of interviews with teachers, librarians, and developing world researchers that provide details about users' collaborative search habits in shared-computer settings, revealing several limitations of this practice. We then introduce CoSearch, a system we developed to improve the experience of co-located collaborative Web search by leveraging readily available devices such as mobile phones and extra mice. Finally, we present an evaluation comparing CoSearch to status quo collaboration approaches, and show that CoSearch enabled distributed control and division of labor, thus reducing the frustrations associated with shared-computer searches, while still preserving the positive aspects of communication and collaboration associated with joint computer use.

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Amershi, Saleema, Carenini, Giuseppe, Conati, Cristina, Mackworth, Alan K. and Poole, David (2008): Pedagogy and usability in interactive algorithm visualizations: Designing and evaluating CIspace. In Interacting with Computers, 20 (1) pp. 64-96.

Interactive algorithm visualizations (AVs) are powerful tools for teaching and learning concepts that are difficult to describe with static media alone. However, while countless AVs exist, their widespread adoption by the academic community has not occurred due to usability problems and mixed results of pedagogical effectiveness reported in the AV and education literature. This paper presents our experiences designing and evaluating CIspace, a set of interactive AVs for demonstrating fundamental Artificial Intelligence algorithms. In particular, we first review related work on AVs and theories of learning. Then, from this literature, we extract and compile a taxonomy of goals for designing interactive AVs that address key pedagogical and usability limitations of existing AVs. We advocate that differentiating between goals and design features that implement these goals will help designers of AVs make more informed choices, especially considering the abundance of often conflicting and inconsistent design recommendations in the AV literature. We also describe and present the results of a range of evaluations that we have conducted on CIspace that include semi-formal usability studies, usability surveys from actual students using CIspace as a course resource, and formal user studies designed to assess the pedagogical effectiveness of CIspace in terms of both knowledge gain and user preference. Our main results show that (i) studying with our interactive AVs is at least as effective at increasing student knowledge as studying with carefully designed paper-based materials; (ii) students like using our interactive AVs more than studying with the paper-based materials; (iii) students use both our interactive AVs and paper-based materials in practice although they are divided when forced to choose between them; (iv) students find our interactive AVs generally easy to use and useful. From these results, we conclude that while interactive AVs may not be universally preferred by students, it is beneficial to offer a variety of learning media to students to accommodate individual learning preferences. We hope that our experiences will be informative for other developers of interactive AVs, and encourage educators to exploit these potentially powerful resources in classrooms and other learning environments.

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Amershi, Saleema and Conati, Cristina (2007): Unsupervised and supervised machine learning in user modeling for intelligent learning environments. In: Proceedings of the 2007 International Conference on Intelligent User Interfaces 2007. pp. 72-81.

In this research, we outline a user modeling framework that uses both unsupervised and supervised machine learning in order to reduce development costs of building user models, and facilitate transferability. We apply the framework to model student learning during interaction with the Adaptive Coach for Exploration (ACE) learning environment (using both interface and eye-tracking data). In addition to demonstrating framework effectiveness, we also compare results from previous research on applying the framework to a different learning environment and data type. Our results also confirm previous research on the value of using eye-tracking data to assess student learning.

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