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<html lang="en">
<head>
<meta charset="utf-8">
<!-- Chrome, Firefox OS and Opera -->
<meta name="theme-color" content="#fff">
<!-- Windows Phone -->
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rel="stylesheet">
<title>Yu Yao</title>
</head>
<body>
<!-- The Modal -->
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<p>Bib info has been copied to the clipboard!</p>
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<div><button onclick="changeMenu(2)"><i class="fa fa-home" style="font-size: 22px;"></i> Home</button></div>
<div><button onclick="changeMenu(3)"><i class="fa fa-book" style="font-size: 19px;"></i> Publications</button>
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<img width="140" height="185" class="headshot" id="selfie" data="1359250181trans.png" alt="" />
<table class="icons">
<tr>
<th>
<h1>Yu Yao</h1>
</th>
<th><a href="https://scholar.google.com/citations?user=OkcaMKAAAAAJ&hl=en" target="_blank"><img
src="google_logo.png" height="30px"></a></th>
<th> <a href="https://www.linkedin.com/in/yu-y/" target="_blank"><img src="linkedin_logo.png"
height="30px"></a></th>
<th> <a href="https://github.com/a5507203" target="_blank" height="30px">
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Meeting with Me</button></div> -->
</div>
<br />
<div>
<i> Lecturer in Machine Learning</i><br />
<i> School of Computer Science, The University of Sydney</i><br />
<i> Email: yu.yao@sydney.edu.au</i><br />
</div>
<br />
<P>
I completed my doctoral studies at The University of Sydney, where I studied under Prof. Tongliang Liu and
Prof. Dacheng Tao.
I worked as a postdoctoral fellow at Mohamed bin Zayed University of Artificial Intelligence
and Carnegie Mellon University, collaborating with Prof.
Tongliang Liu and Prof. Kun Zhang.
</P>
<div id="goal">
<hr style="clear: both;">
My goal is to make the machine learning systems to be reliable and aligned with human understanding. This
includes
the following directions:
<ul>
<li>
Robustness:
How can we build ML systems that are robust to different types of noise in different data
modalities?
How can we improve ML systems' adaptability to changing environments?
How can we design the evaluation methods to existing ML systems in real-world settings?
</li>
<li>
Interpretable Representation: How can we explain the representation learned by different ML methods?
What would be the necessary assumptions for developing disentangled representations under different
cases?
How can we use multimodal data together to encourage the disentanglement?
What empirical methods are needed to effectively achieve such disentanglement?
</li>
</ul>
Targeting these challenges by combining rigorous theoretical frameworks with empirical insights is not
only provide us with a clearer understanding of ML systems, but is necessary if we hope for
ML to truly benefit society.
<!--
Multi-Agent Systems: What strategies can be developed to refine agent interaction protocols, reduce context-window dependencies, and create adaptive workflows that
mirror the efficiency and nuances of human collaboration? How can we optimize collaborative workflows in multi-agent systems
to improve problem-solving capabilities with minimal human supervision? -->
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<h2>Updates</h2>
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<h2>Students</h2>
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<h2>Publications</h2>
</div>
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<h2>Teaching & Talk Experience</h2>
</div>
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<h2>Professional Services</h2>
</div>
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<hr style="clear: both;">
<h2>Honors and Awards</h2>
</div>
</div>
</body>
</html>