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    <header>
        <h1><a href="/" class="name-link" aria-current="page">Bodun Hu</a></h1>
        <span aria-hidden="true">|</span>
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            <a href="https://www.bodunhu.com/blog/">Blog</a>
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        <p>I am a software engineer at <a href="https://techsysinfra.google/">Google</a>, where I work on improving the reliability of Gemini training. My research focuses on high-performance systems for machine learning, particularly LLM training and serving, with interests spanning operating systems, networking, and compute-communication co-design.</p>

        <p>I earned my Ph.D. in Computer Science from <a href="https://www.utexas.edu/">UT Austin</a>, advised by <a href="https://www.cs.utexas.edu/~akella/">Aditya Akella</a> in the <a href="https://utns.cs.utexas.edu/">UTNS</a> lab. I also received my M.S. and B.S. in Computer Science from UT Austin. Before my Ph.D., I worked
            with <a href="https://www.cs.utexas.edu/~rossbach/">Christopher Rossbach</a> in the
            SCEA lab on GPU benchmarking and methods for enabling OS kernels to access accelerators.</p>

        <p>I previously interned at Intel with <a href="https://theojepsen.dk/">Theo Jepsen</a> and
            <a href="https://www.linkedin.com/in/georgiosnikolaidis">Georgios Nikolaidis</a>, and at Meta's
            <a href="https://aisystemcodesign.github.io/">AI and Systems Co-design team</a>, where I developed efficient communication collectives for LLM training under the supervision of <a href="https://kingchc.gitlab.io/">Ching-Hsiang Chu</a>.</p>

        <p>I occasionally share notes and write-ups on my <a href="https://www.bodunhu.com/blog/">blog</a>.</p>

        <p>Contact: <em>bodunhu at gmail.com</em><br>
        PGP key: <a href="https://keys.openpgp.org/vks/v1/by-fingerprint/A1B9B452CA88126F31C3D3E276F092F8CD673517">76F092F8CD673517</a></p>

        <h2>Publications</h2>

        <ul>
			<li><a href="https://arxiv.org/pdf/2603.02376">CUCo: An Agentic Framework for Compute and Communication Co-design</a>, Preprint<br>
                <em><strong>Bodun Hu</strong>*, Yoga Sri Varshan V*, Saurabh Agarwal, Aditya Akella</em>
            </li>
			<li><a href="https://arxiv.org/pdf/2602.00085">CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models</a>, Preprint<br>
                <em>Shuozhe Li, Jincheng Cao, <strong>Bodun Hu</strong>, Aryan Mokhtari, Leqi Liu, Amy Zhang</em>
            </li>
            <li><a href="https://www.usenix.org/conference/nsdi26/presentation/agarwal">SYMPHONY: Enabling Compute-Memory Disaggregation in LLM Serving Systems</a>, NSDI 2026<br>
                <em>Saurabh Agarwal, <strong>Bodun Hu</strong>, Anyong Mao, Aditya Akella, Shivaram Venkataraman</em>
            </li>
            <li><a href="https://arxiv.org/pdf/2505.13444">ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models</a>, NeurIPS 2025<br>
                <em>Liyan Tang, Grace Kim, Xinyu Zhao, Thom Lake, Wenxuan Ding, Fangcong Yin, Prasann Singhal, Manya Wadhwa, Zeyu Leo Liu, Zayne Sprague, Ramya Namuduri, <strong>Bodun Hu</strong>, Juan Diego Rodriguez, Puyuan Peng, Greg Durrett</em>
            </li>
            <li><a href="https://arxiv.org/pdf/2505.07833">Patchwork: A Unified Framework for RAG Serving</a>, ArXiv<br>
                <em><strong>Bodun Hu</strong>*, Luis Pabon*, Saurabh Agarwal, Aditya Akella</em>
            </li>
            <li><a href="https://aclanthology.org/2025.acl-long.1305.pdf">StitchLLM: Serving LLMs, One Block at a Time</a>, ACL 2025<br>
                <em><strong>Bodun Hu</strong>, Shuozhe Li, Saurabh Agarwal, Myungjin Lee, Akshay Jajoo, Jiamin Li, Le Xu, Geon-Woo Kim, Donghyun Kim, Hong Xu, Amy Zhang, Aditya Akella</em>
            </li>
            <li><a href="https://aclanthology.org/2024.emnlp-main.501.pdf">MOSEL: Inference Serving Using Dynamic Modality Selection</a>, EMNLP 2024<br>
                <em><strong>Bodun Hu</strong>, Le Xu, Jeongyoon Moon, Neeraja J. Yadwadkar, Aditya Akella</em>
            </li>
            <li><a href="https://aclanthology.org/2024.emnlp-main.941.pdf">FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping</a>, EMNLP 2024<br>
                <em>Ajay Jaiswal, <strong>Bodun Hu</strong>, Lu Yin, Yeonju Ro, Shiwei Liu, Tianlong Chen, Aditya Akella</em>
            </li>
            <li><a href="https://dl.acm.org/doi/pdf/10.1145/3575693.3575697">Towards a Machine Learning-Assisted Kernel with LAKE</a>, ASPLOS 2023<br>
                <em>Henrique Fingler, Isha Tarte, Hangchen Yu, Ariel Szekely, <strong>Bodun Hu</strong>, Aditya Akella, Christopher J. Rossbach</em>
            </li>
            <li><a href="https://ieeexplore.ieee.org/document/9238617">Altis: Modernizing GPGPU Benchmarks</a>, ISPASS 2020<br>
                <em><strong>Bodun Hu</strong>, Christopher J. Rossbach</em>
            </li>
        </ul>

        <h2>Teaching</h2>

        <ul>
			<li><a href="https://utnslab.github.io/fa25-adv-topics-in-sys-genai/">Fall 2025: CS 395T: Advanced Topics in Systems and GenAI</a>
            </li>
            <li><a href="https://www.cs.utexas.edu/~akella/CS378/F24/index.html">Fall 2024: CS 378 System For Machine Learning and Big Data</a>
            </li>
            <li><a href="https://www.cs.utexas.edu/~simon/378/">Spring 2020: CS 378 Multicore Operating System Implementation</a>
            </li>
        </ul>

        <h2>Service</h2>

        <p>Reviewer for ACL 2025, ACL 2026, ACL-SRW 2025, ICLR 2026, CAIS 2026, NeurIPS 2026, DocInsights 2026, and ICLR 2027.</p>

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