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| <script type="text/javascript" async src="fonts-min.js"></script> | ||
| <link rel="stylesheet" href="style.css" /> | ||
| <meta charset="utf-8"> | ||
| <meta name="google-site-verification" content="QNN8Jc85QU2H26Z6heRpiaDazA20lmmFAfwYrZ3tYR4" /> | ||
| <meta name="description" content="Hyunsu Cho's research profile" /> | ||
| <meta name="description" content="Hyunsu Cho's website" /> | ||
| <title>Hyunsu (Philip) Cho</title> | ||
| </head> | ||
| <body> | ||
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@@ -18,63 +17,80 @@ <h1 itemprop="name" id="name">Hyunsu (Philip) Cho</h1> | |
| </div> | ||
| <div id="my_contact"> | ||
| <p> | ||
| <span itemscope | ||
| itemtype="http://schema.org/EducationalOrganization"> | ||
| <span itemprop="name"> | ||
| <a href="https://www.cs.washington.edu/">Paul G. Allen School<br>of Computer Science and Engineering<br>University of Washington</a> | ||
| </span> | ||
| </span><br> | ||
| CSE 318<br> | ||
| <a href="mailto:[email protected]">[email protected]</a> | ||
| Applied Scientist | ||
| <br> | ||
| <a href="https://aws.amazon.com/">Amazon Web Services</a> | ||
| <br> | ||
| Seattle, WA, USA | ||
| </p> | ||
| </div> | ||
| <div class="clearboth"> </div> | ||
| </div> | ||
| <div id="nav"> | ||
| <ul> | ||
| <li><a href="cv/cv.pdf">CV</a></li> | ||
| <li>·</li> | ||
| <li><a href="#works">Recent works</a></li> | ||
| <li>·</li> | ||
| <li><a href="#experience">Experience</a></li> | ||
| <li>·</li> | ||
| <li><a href="#research">Publications</a></li> | ||
| <li>·</li> | ||
| <li><a href="#awards">Awards</a></li> | ||
| </ul> | ||
| </div> | ||
| <div id="profile-pic"> | ||
| <img src="pic.jpg" width="150" height="150" | ||
| alt="Philip's picture" itemprop="image" /> | ||
| </div> | ||
| <p> | ||
| I am a second-year Ph.D. student at Paul G. Allen School. My primary research interest is building efficient systems for machine learning. | ||
| My primary research interest is building efficient systems for machine | ||
| learning. I have recently started my role as an Applied Scientist at | ||
| Amazon Web Services. | ||
| </p> | ||
| <h2 id="works">Recent works</h2> | ||
| <p>You may access my CV <a href="cv/cv.pdf">here</a>.</p> | ||
| <p>Contact me at chohyu01 (at) cs.washington.edu.</p> | ||
| <h2 id="works">Open-source Involvement</h2> | ||
| <ul> | ||
| <li> | ||
| Owner and Maintainer of | ||
| <strong><a href="https://github.com/dmlc/treelite">Treelite</a></strong> | ||
| <div class="vspace"></div> | ||
| Treelite is a framework for easy and efficient deployment of | ||
| <a href="https://en.wikipedia.org/wiki/Decision_tree_learning">decision tree | ||
| ensembles</a>. The project is in its early stage now; I hope to recruit more | ||
| contributors in the future. Any feedback is welcome! | ||
| </li> | ||
| <li> | ||
| Contributor and Maintainer of | ||
| <strong><a href="https://github.com/dmlc/xgboost">XGBoost</a></strong> | ||
| <div class="vspace"></div> | ||
| I have submitted <a href="https://github.com/dmlc/xgboost/pull/1940">a major addition</a> to XGBoost, featuring faster training time and flexible tree-growing | ||
| strategy. XGBoost is a widely adopted machine learning package for scalable <a href="https://en.wikipedia.org/wiki/Gradient_boosting">gradient boosting</a>. | ||
| XGBoost is an efficient, scalable framework for | ||
| <a href="https://en.wikipedia.org/wiki/Gradient_boosting">gradient boosting</a>. | ||
| </li> | ||
| </ul> | ||
| <h2 id="experience">Experience</h2> | ||
| <ul> | ||
| <li> | ||
| <strong>Applied Scientist</strong> | ||
| <div class="vspace"></div> | ||
| Amazon Web Services. January 2018-Present. | ||
| </li> | ||
| <li> | ||
| <strong>Applied Scientist Intern</strong> | ||
| <div class="vspace"></div> | ||
| AWS Deep Learning team, Amazon. June-September 2017. | ||
| Amazon Web Services. June-December 2017. | ||
| </li> | ||
| <li> | ||
| <strong>Teaching Assistant</strong> | ||
| <div class="vspace"></div> | ||
| Machine Learning Specialization at Coursera. 2015-2017. | ||
| </li> | ||
| <li> | ||
| <strong>Undergraduate Research Assistant</strong> | ||
| <div class="vspace"></div> | ||
| Trinity college. 2012-2015. | ||
| </li> | ||
| </ul> | ||
| <h2 id="research">Peer-reviewed publications</h2> | ||
| <ul> | ||
| <li> | ||
| <strong>Treelite: toolbox for decision tree deployment</strong> | ||
| <br> | ||
| with | ||
| <a href="https://github.com/mli">Mu Li</a> | ||
| <div class="vspace"></div> | ||
| Paper to be presented at | ||
| <a href="http://www.sysml.cc">SysML 2018</a> (2018) | ||
| </li> | ||
| <li> | ||
| <strong><a href="hypergraph.html"> | ||
| An accelerated procedure for hypergraph coarsening on the GPU | ||
| </a> | ||
|
|
@@ -147,7 +163,7 @@ <h2 id="awards">Awards and Honors</h2> | |
| Computing Research Association, 2015.</li> | ||
| <li> | ||
| <strong>Recipient of | ||
| <a href="https://goldwater.scholarsapply.org/"> | ||
| <a href="https://goldwater.scholarsapply.org/2014-scholars/"> | ||
| the Goldwater Scholarship</a></strong>, | ||
| The United States Congress, 2014.</li> | ||
| <li> | ||
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