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<!DOCTYPE html>
<!-- saved from url=(0034)http://people.inf.ethz.ch/alucchi/ -->
<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Yanzhi Chen</title>
<meta name="description" content="website description">
<meta name="keywords" content="machine learning">
<link rel="stylesheet" type="text/css" href="files/style.css">
<link rel="icon" href="files/favicon.png">
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-52441923-2');
</script>
</head>
<body>
<div id="main">
<div id="header">
<div id="menubar">
<ul id="menu">
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<li class="selected"><a href="index.html">Home</a></li>
<li><a href="publications.html">Publications</a></li>
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</ul>
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<h2>Yanzhi Chen</h2>
<p>I am a PhD student at <em> Cambridge MLG </em> supervised by <a href="http://mlg.eng.cam.ac.uk/adrian/">Dr. Adrian Weller</a> fully funded by the Cambridge Trust. Previously, I graduated from the MSc AI programme of <em> The University of Edinburgh </em>, and I obtained my BEng in Computer Science from <em> South China University of Technology </em>. Prior to my MSc, I spent years in <em class="tooltip"> Alibaba Group <span class="tooltiptext"> the largest internet corporation in China </span> </em> working on autonomous cloud. I also work closely with <a href="http://yingzhenli.net/home/en/"> Dr. Yingzhen Li </a> and <a href="https://michaelgutmann.github.io/"> Dr. Michael Gutmann </a>. </p>
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<p>
<b>Information</b>
<br>
E-mail: yc514 [AT] cam.ac.uk <br>
Github: <a href="https://github.com/cyz-ai">cyz-ai</a> <br>
Google scholar: <a href="https://scholar.google.com/citations?user=W5sSgIsAAAAJ&hl=en">link</a> <br>
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<h3>Research interests</h3>
<li class="tooltip"> Deep probabilistic models <span class="tooltiptext">e.g VAE/GAN, flows, energy/score-based model </span> </li>
<li class="tooltip"> Trustworthy AI <span class="tooltiptext">e.g fairness, adversarial robustness, XAI, uncertainty </span> </li>
<li class="tooltip"> Approximate inference <span class="tooltiptext">e.g variational inference, likelihood-free inference </span> </li>
<!-- <li class="tooltip"> Deep neural networks <span class="tooltiptext">e.g adversarial robustness, generalization, interpretability </span> </li> -->
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