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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Kaizhao Liang - AI Researcher</title>
<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;700&display=swap" rel="stylesheet">
<script src="https://kit.fontawesome.com/a076d05399.js"></script>
<style>
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line-height: 1.6;
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.main-content {
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padding: 20px;
margin-top: 20px;
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@media(max-width: 768px) {
header {
padding-bottom: 20px;
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.container {
width: 95%;
}
}
</style>
</head>
<body>
<header>
<div class="container">
<h1><span class="highlight">Kaizhao Liang</span></h1>
</div>
</header>
<div class="container">
<div class="profile">
<img src="images/profile.jpg" alt="Kaizhao Liang">
<h2>Kaizhao Liang</h2>
<p>AI Researcher | Austin, Texas, United States</p>
<div class="social-links">
<a href="https://twitter.com/KyleLiang5"><i class="fab fa-twitter"></i></a>
<a href="https://github.com/kyleliang919"><i class="fab fa-github"></i></a>
<a href="https://github.com/kyleliang919/kyleliang919.github.io/blob/main/Kaizhao_s_Resume.pdf"><i class="fas fa-file-alt"></i></a>
</div>
</div>
<div class="main-content">
<h2>About</h2>
<p>Coding, Life, and AGI</p>
<h2>Interests</h2>
<p>Teaching AI how to reason about the world</p>
<div class="highlight-work">
<h3>Recent Highlight: SambaNova's Llama-3.1-Instruct-O1</h3>
<p>I built SambaNova's recent work challenging OpenAI's O1 model with a Llama 3.1-powered demo. Check out our implementation on HuggingFace:</p>
<a href="https://huggingface.co/spaces/sambanovasystems/Llama3.1-Instruct-O1" target="_blank">Llama3.1-Instruct-O1 Demo</a>
<p>Read more about this development in the <a href="https://venturebeat.com/ai/sambanova-challenges-openais-o1-model-with-llama-3-1-powered-demo-on-huggingface/" target="_blank">VentureBeat article</a>.</p>
</div>
<h2>Selected Publications</h2>
<div class="publication">
<a href="https://www.arxiv.org/abs/2408.12857">Memory-Efficient LLM Training with Online Subspace Descent [NeurIPS 2024] </a>
<p><strong>Kaizhao Liang</strong>, Bo Liu, Lizhang Chen, Qiang Liu</p>
</div>
<div class="publication">
<a href="https://arxiv.org/abs/2112.00029">Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models [ICLR 2022] </a>
<p>Beidi Chen, Tri Dao, <strong>Kaizhao Liang</strong>, Jiaming Yang, Zhao Song, Atri Rudra, Christopher Re</p>
</div>
<div class="publication">
<a href="https://arxiv.org/abs/2006.14512">Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability [ICML 2020] </a>
<p><strong>Kaizhao Liang*</strong>, Jacky. Y. Zhang*, Boxin Wang, Zhuolin Yang, Sanmi Koyejo, Bo Li</p>
</div>
<h2>Relevant Projects</h2>
<div class="project">
<a href="https://github.com/kyleliang919/Gesture-Control-of-Autonomous-Vehicle">Gesture Control of Autonomous Vehicle</a>
<p>A new interface for human interaction with Autonomous Vehicles.</p>
</div>
<div class="project">
<a href="https://github.com/kyleliang919/Interval-bound-propagation-pytorch">Interval Bound Propagation PyTorch</a>
<p>Exploring certified defense against adversarial attacks with limited resources by using a "tighter" objective.</p>
</div>
<h2>Review Service</h2>
<p>CVPR2021, ICCV2021, ICLR2022, CVPR2022, AISTATS2022, NEURIPS2023, ICLR2023, CVPR2023</p>
<h2>Contact</h2>
<p>[email protected]</p>
<p>[email protected]</p>
</div>
</div>
</body>
</html>