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<!DOCTYPE HTML>
<!--
Read Only by HTML5 UP
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<html>
<head>
<title>Amanda Swearngin</title>
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<body>
<!-- Header -->
<section id="header">
<header>
<span class="image avatar"><img src="images/avatar.jpg" alt="" /></span>
<h1 id="logo"><a href="#">Amanda Swearngin</a></h1>
<p>Ph.D. Student in Computer Science</p>
<p>University of Washington</p>
</header>
<nav id="nav">
<ul>
<li><a href="#one" class="active">About</a></li>
<li><a href="#two">Research</a></li>
<li><a href="#three">Publications</a></li>
<li><a href="#four">Service</a></li>
<li><a href="#five">Contact</a></li>
</ul>
</nav>
<footer>
<ul class="icons">
<li><a href="https://www.facebook.com/mandamarie05" class="icon fa-facebook"><span class="label">Facebook</span></a></li>
<li><a href="https://github.com/mandamarie0587" class="icon fa-github"><span class="label">Github</span></a></li>
<li><a href="mailto:amaswea@cs.washington.edu" class="icon fa-envelope"><span class="label">Email</span></a></li>
</ul>
</footer>
</section>
<!-- Wrapper -->
<div id="wrapper">
<!-- Main -->
<div id="main">
<!-- One -->
<section id="one">
<div class="container">
<header class="major">
<h2>About Me</h2>
</header>
<p>I am a final year Ph.D. candidate in Computer Science at the University of Washington,
advised by <a href="https://faculty.washington.edu/ajko/">Amy Ko</a> and
<a href="https://homes.cs.washington.edu/~jfogarty/">James Fogarty</a>. I am currently a
Research Intern at Microsoft Research, working with
<a href="https://www.microsoft.com/en-us/research/people/shamsi/">Shamsi Iqbal</a>.
I have previously completed research internships with Adobe Research and Google. I also spent 3 years working as a software engineer and SDET at Microsoft,
where I worked on a web interface framework for Dynamics AX. I am currently seeking Research Engineering
positions in industry.
<br /><br />I am interested in data-driven design and in building interactive tools that enhance
the capabilities of interface designers through applications of program synthesis, constraint solving,
and machine learning. Here is a link to my current <a href="assets/documents/AmandaSwearnginCV.pdf">CV</a>
<i class="fa fa-file-pdf-o">
</i>
</p>
</div>
</section>
<!-- Two -->
<section id="two">
<div class="container">
<header class="major">
<h2>Current Research</h2>
</header>
<div class="project-container">
<div class="project-header">
<h3><a href="#scout">Scout: Mixed-Initiative Exploration of Design Variations through High-Level Design Constraints</a></h3>
</div>
<div class="project-content">
<p class="project-text">
Although the exploration of variations is a key part of interface design, current processes for creating variations are mostly manual. Scout system that helps designers explore many variations rapidly through mixed-initiative interaction with high-level constraints and design feedback. Scout allows designers to specfiy high-level constraints based on design concepts (e.g. emphasis). We have formalized several of these high-level constraints into their corresponding low-level spatial constraints to enable rapidly generating many designs through constraint solving and program synthesis.
</p>
<div class="project-video">
<img class="scout-image" src="images/scout.png" />
</div>
</div>
</div>
<br /><br />
<div class="project-container">
<div class="project-header">
<h3><a href="#scout">TapShoe: Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning</a></h3>
</div>
<div class="project-content">
<p class="project-text">
Tapping is an immensely important gesture in mobile touchscreen interfaces, yet people still frequently are required to learn which elements are tappable through trial and error. Predicting human behavior for this everyday gesture can help mobile app designers understand an important aspect of the usability of their apps without having to run a user study. TapShoe is a deep learning model and approach for modeling the tappability of mobile interfaces at scale. For this project, we conducted large-scale data collection of tappability annotations using crowdsourcing and computationally investigated signifiers that people use to distinguish tappable versus not-tappable elements. We built a deep learning model to predict interface elements people will perceive as tappable and not tappable and created an interface that identifies mismatches between the predicted tappable state of an element and the actual tappable state in code.
</p>
<div class="project-video">
<img class="tap-shoe-image" src="images/tap_shoe.png" />
</div>
</div>
</div>
</div>
<!-- <h3>Change detection for the web</h3>
<p> I built a system for automatic change detection on the web, and for interactive change exploration using automated program analysis.</p> -->
<div class="container">
<header class="major">
<h2>Past Research</h2>
</header>
<div class="project-container">
<div class="project-header">
<h3><a href="#rewire">Rewire: Interface Design Assistance from Examples</a></h3>
</div>
<div class="project-content">
<p class="project-text">
Interface designers often use screenshot images of example designs as building blocks for new designs. Since images are unstructured and hard to edit, designers typically reconstruct screenshots with vector graphics tools in order to reuse or edit parts of the design. Unfortunately, this reconstruction process is tedious and slow. Rewire is an interactive system that helps designers leverage example screenshots. Rewire automatically infers a vector representation of screenshots where each UI component is a separate object with editable shape and style properties. Rewire provides three design assistance modes that help designers reuse or redraw components of the example design.
</p>
<div class="project-video" style="position:relative;width:360px;height:200px;"><iframe src="https://www.youtube.com/embed/ifiwpBe_jkA?ecver=2" width="360" height="200" frameborder="0" allow="autoplay; encrypted-media" style="position:absolute;width:100%;height:100%;left:0" allowfullscreen></iframe></div>
</div>
</div>
<div class="project-container">
<div class="project-header">
<h3><a href="#genie">Genie: Input Retargeting on the Web through Command Reverse Engineering</a></h3>
</div>
<div class="project-content">
<p class="project-text">
I created an abstract model of a command and a set of methods for reverse engineerings commands and command metadata from arbitrary web applications for the purposes of command monitoring and retargeting inputs to alternate modalities (e.g., Retargeting a web application built only for mouse input to have audio-controlled commands). The system uses JavaScript static and dynamic program analysis to discover commands and monitor their status, and is built in a Chrome Extension. I am working on creating an open source version of the tool and will be making it available soon (TBA).
</p>
<div class="project-video" style="position:relative;width:360px;height:270px;"><iframe src="https://www.youtube.com/embed/mQlr1kkitF8?ecver=2" width="360" height="270" frameborder="0" style="position:absolute;width:100%;height:100%;left:0" allowfullscreen></iframe></div>
</div>
</div>
<div class="project-container">
<div class="project-header">
<h3><a href="#cogtool-helper">CogTool-Helper: Generating Predictive Human Performance Models from Interfaces</a></h3>
</div>
<div class="project-content">
<p class="project-text">
I built a system called CogTool-Helper that automatically infers a model of an interface and generates storyboards and cognitive models that allow UI designers to estimate human task performance in an interface. This system combines tools from software engineering for GUI testing (<a href="https://sourceforge.net/projects/guitar/">GUITAR</a>) with <a href="https://cogtool.wordpress.com/overview-of-cogtool/">CogTool</a>, a system for human performance modeling. </p>
<img class="project-cth" src="images/cogtoolhelper.png" alt="" />
</div>
</div>
</div>
</section>
<!-- Three -->
<section id="three">
<div class="container">
<header class="major">
<h2>Publications</h2>
</header>
<h3>Conference Publications</h3>
<ul class="feature-icons">
<li class="fa-file-text fa-clickable"><a id="rewire" class="paper" href="assets/documents/AmandaSwearngin_YangLi_TapShoe_CHI2019.pdf"><span class="paper-first-author">Amanda Swearngin</span>, Yang Li. TapShoe: Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), 2019, <i>Conditionally Accepted</i>.</a></li>
<li class="fa-file-text fa-clickable"><a id="rewire" class="paper" href="assets/documents/AmandaSwearngin_Rewire_CHI2018.pdf"><span class="paper-first-author">Amanda Swearngin</span>, Mira Dontcheva, Wilmot Li, Joel Brandt, Morgan Dixon, Andrew J. Ko. Rewire: Interface Design Assistance from Examples. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), 2018.</a></li>
<li class="fa-file-text fa-clickable"><a id="genie" class="paper" href="assets/documents/AmandaSwearngin_CHI2017_camera_ready.pdf"><span class="paper-first-author">Amanda Swearngin</span>, Andrew J. Ko, James Fogarty. Genie: Input Retargeting on the Web through Command Reverse Engineering. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), 2017.</a> </li>
<li class="fa-file-text fa-clickable"><a id="cogtool-helper" class="paper" href="assets/documents/AmandaSwearngin_ICSE2013.pdf"><span class="paper-first-author">Amanda Swearngin</span>, Myra B. Cohen, Bonnie E. John, Rachel K.E. Bellamy. Human Performance Regression Testing. International Conference on Software Engineering (ICSE), pages 152-161, 2013. (acceptance rate: 18.5%)</a></li>
<li class="fa-file-text fa-clickable"><a class="paper" href="assets/documents/AmandaSwearngin_CHI2012.pdf"><span class="paper-first-author">Amanda Swearngin</span>, Myra B. Cohen, Bonnie E. John, Rachel K.E. Bellamy. Easing the Generation of Predictive Human Performance Models from Legacy Systems. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pages 2489-2498, 2012. (acceptance rate: 23%)</a></li>
<li class="fa-file-text fa-clickable"><a class="paper" href="assets/documents/Kuttal_ISEUD2011.pdf">Sandeep Kaur Kuttal, Anita Sarma, <span class="paper-author">Amanda Swearngin</span>, Gregg Rothermel. Versioning for Mashups — An Exploratory Study. International Symposium on End User Development (IS-EUD), pages 25-41, 2011. (acceptance rate: 27%)</a></li>
<li class="fa-file-text fa-clickable"><a class="paper" href="assets/documents/AmandaSwearngin_SARA2011.pdf"><span class="paper-first-author">Amanda Swearngin</span>, Berthe Y. Choueiry, Eugene C. Freuder. A Reformulation Strategy for Multi-Dimensional CSPs: The Case Study of the SET Game. Symposium on Abstraction, Reformulation, and Approximation (SARA), pages 107-116, 2011. </a></li>
</ul>
<h3>Demos & Extended Abstracts</h3>
<ul class="feature-icons">
<li class="fa-file-text fa-clickable"><a id="scout" class="paper" href="assets/documents/Swearngin_UIST18_Demo_Scout.pdf"><span class="paper-first-author">Amanda Swearngin</span>, Andrew J. Ko, James Fogarty. Scout: Mixed-Initiative Exploration of Design Variations through High-Level Design Constraints. ACM User Interface Software and Technology Symposium (UIST) '18, Demos.</a></li>
</ul>
<h3>Patents</h3>
<ul class="feature-icons">
<li class="fa-file-text fa-clickable"><a class="paper" href="https://www.google.com/patents/US8903690">Linking graphical user interface testing tools and human performance modeling to enable usability assessment, Rachel K. E. Bellamy, Myra B. Cohen, Bonnie E. John, Padmanabhan Santhanam, Amanda Swearngin, US Patent App. 13/672,237, 2012</a></li>
</ul>
</div>
</section>
<section id="four">
<div class="container">
<header class="major">
<h2>Service</h2>
</header>
<h3>Reviewing</h3>
<div>EICS PACM (2018), Transactions on Software Engineering (TSE) 2018, CHI 2019, UIST 2019,
<br />Creativity & Cognition 2019
</div>
</div>
</section>
<!-- Four -->
<section id="five">
<div class="container">
<header class="major">
<h2>Contact</h2>
</header>
<p>You can contact me at <b>amaswea@cs.washington.edu</b>, or find me in the Paul Allen Center for CSE, Room 605.</p>
</div>
</section>
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