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          <p class="eyebrow">Senior Research Scientist / Amazon AGI Foundations / AWS</p>
          <h1>Boran <span>Han.</span></h1>
          <p class="hero-summary">
            Building foundation models and learning systems for agentic tasks,
            structured data, and multimodal reasoning.
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            <a class="button primary" href="/research/">View research</a>
            <a class="button" href="https://scholar.google.com/citations?user=Prwxh24AAAAJ" target="_blank" rel="noreferrer">Google Scholar</a>
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        <span class="hero-index">Santa Clara, California / 37.35 N</span>
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            <p class="section-label">Research focus / 01</p>
            <h2>Foundation models and reasoning systems for agents, structured data, and science.</h2>
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              <img class="research-card-diagram" src="/assets/images/reskill.webp" alt="ReSkill framework for creating and testing reusable skills during agentic reinforcement learning" loading="lazy" decoding="async">
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                <p class="card-kicker">Agentic RL / 2026</p>
                <h3>Skills that evolve with the policy</h3>
                <p>Reconciling reusable skill creation with reward-driven policy optimization.</p>
                <a class="card-link" href="https://arxiv.org/abs/2606.01619" target="_blank" rel="noreferrer" aria-label="Read the ReSkill paper"></a>
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              <img src="/assets/images/sentsr.webp" alt="SenTSR-Bench knowledge injection framework for time-series reasoning" loading="lazy" decoding="async">
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                <p class="card-kicker">Reasoning / 2026</p>
                <h3>Knowledge injection for time-series reasoning</h3>
                <p>Bridging specialist time-series models and general reasoning models.</p>
                <a class="card-link" href="https://arxiv.org/abs/2602.19455" target="_blank" rel="noreferrer" aria-label="Read SenTSR-Bench"></a>
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              <img class="research-card-diagram" src="/assets/images/chronos-2.webp" alt="Chronos-2 architecture for universal time-series forecasting" loading="lazy" decoding="async">
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                <p class="card-kicker">Foundation models / 2025</p>
                <h3>Universal zero-shot forecasting</h3>
                <p>One model for univariate, multivariate, and covariate-informed time series.</p>
                <a class="card-link" href="https://huggingface.co/amazon/chronos-2" target="_blank" rel="noreferrer" aria-label="Explore Chronos-2"></a>
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              <img class="research-card-diagram" src="/assets/images/hetrl.webp" alt="HetRL scheduling and execution framework for reinforcement learning across heterogeneous GPU environments" loading="lazy" decoding="async">
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                <p class="card-kicker">Systems / MLSys 2026</p>
                <h3>RL across heterogeneous hardware</h3>
                <p>Efficient LLM post-training across mixed GPUs, regions, and interconnects.</p>
                <a class="card-link" href="https://arxiv.org/abs/2512.12476" target="_blank" rel="noreferrer" aria-label="Read the HetRL paper"></a>
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            <p class="section-label">By the numbers / 02</p>
            <h2>Research metrics.</h2>
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              <strong>3K+</strong>
              <span>citations across machine learning, imaging, and natural science</span>
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            <div class="stat">
              <strong>30</strong>
              <span>selected publications in leading conferences and journals</span>
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              <strong>149M+</strong>
              <span>total downloads for Chronos-2</span>
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              <strong>11.7M+</strong>
              <span>combined total downloads across Mitra models</span>
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            <p class="section-label">Recent work / 03</p>
            <h2>Recent publications.</h2>
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            <a class="latest-item" href="https://arxiv.org/abs/2609.04540" target="_blank" rel="noreferrer">
              <time datetime="2026">2026</time>
              <h3>Mitra-v2 Technical Report</h3>
              <span>Technical report</span>
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            <a class="latest-item" href="https://arxiv.org/abs/2606.32029" target="_blank" rel="noreferrer">
              <time datetime="2026">2026</time>
              <h3>When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors</h3>
              <span>ACL Oral</span>
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            <a class="latest-item" href="https://arxiv.org/abs/2606.18388" target="_blank" rel="noreferrer">
              <time datetime="2026">2026</time>
              <h3>LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents</h3>
              <span>Preprint</span>
            </a>
            <a class="latest-item" href="https://arxiv.org/abs/2606.01619" target="_blank" rel="noreferrer">
              <time datetime="2026">2026</time>
              <h3>ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL</h3>
              <span>Preprint</span>
            </a>
            <a class="latest-item" href="https://arxiv.org/abs/2602.19455" target="_blank" rel="noreferrer">
              <time datetime="2026">2026</time>
              <h3>SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning</h3>
              <span>AISTATS</span>
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