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hyeondo-janghyeondo-jang
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Refine research focus interactions
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‎gallery.html‎

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‎group.html‎

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‎hiring.html‎

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‎index.html‎

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‎publications.html‎

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‎research.html‎

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.research-intro {
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<main>
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<h2>Research Focus</h2>
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<p class="research-intro">
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Our lab focuses on the research directions below. Select more for each topic to expand its description.
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</p>
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<div class="section">
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<img src="research_focus/opt-model.png" alt="Model Compression">
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<img src="research_focus/opt-compression.svg" alt="Model Compression">
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<div class="section-text">
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<p>
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<span class="red">Model Compression</span>
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As AI models become larger and more computationally demanding, compression is essential for making them practical and sustainable.
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We develop optimization methods that reduce training and inference costs while preserving model performance.
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Our work focuses on extreme compression across the AI model lifecycle.
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<button class="fold-trigger" type="button" aria-expanded="false" aria-controls="compressionDetail">
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<span class="fold-label">more</span><span class="fold-caret" aria-hidden="true">🔍</span>
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</button>
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</p>
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<div class="fold-detail" id="compressionDetail">
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<div class="fold-item">
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<span class="fold-item-title">Extreme Sparsity <span class="paper-tag">(<a href="https://arxiv.org/abs/2510.01650" target="_blank">ICLR 2026</a>)</span></span>
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We push the limits of LLM sparsity through advanced optimization frameworks such as ADMM, aiming for robust generalization even at extreme compression levels.
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</div>
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<div class="fold-item">
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<span class="fold-item-title">Joint Compression</span>
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We develop unified frameworks that integrate pruning, quantization, and distillation to reduce error accumulation and preserve core model capabilities, including reasoning, in highly compressed states.
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</div>
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</div>
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</div>
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</div>
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The shift toward massive-scale models has made distributed training essential for workloads that no single machine can support.
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From an optimization perspective, the key challenge is maintaining efficiency while managing the overhead of communication between devices.
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Our research develops robust algorithms for decentralized and high-latency training environments.
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<button class="fold-trigger" type="button" aria-expanded="false" aria-controls="distributedDetail">
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<span class="fold-label">more</span><span class="fold-caret" aria-hidden="true">🔍</span>
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</button>
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</p>
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<div class="fold-detail" id="distributedDetail">
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<div class="fold-item">
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<span class="fold-item-title">Communication-Efficient Training <span class="paper-tag">(<a href="https://arxiv.org/abs/2602.18181" target="_blank">arXiv 2026</a>)</span></span>
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We design methods that reduce the heavy data-sharing requirements of distributed training through information compression, reduced synchronization, and zeroth-order optimization.
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</div>
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<div class="fold-item">
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<span class="fold-item-title">Asynchronous Training <span class="paper-tag">(<a href="https://arxiv.org/abs/2602.03515" target="_blank">arXiv 2026</a>)</span></span>
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We create algorithms that allow devices to work independently and reduce waiting time, with a focus on correcting errors caused by slightly outdated information.
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</div>
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<div class="fold-item">
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<span class="fold-item-title">Device-Heterogeneous Training</span>
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We develop adaptive strategies for imbalanced computational resources across devices, aiming for consistent global convergence despite variations in hardware performance.
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</div>
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</div>
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</div>
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</div>
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<div class="section-text">
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<p>
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<span class="blue">Deep Learning Applications</span>
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Optimization is not only a subject of study in itself, but also a versatile lens through which we tackle diverse challenges arising in real-world deep learning systems.
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Optimization is not only a subject of study in itself, but also a versatile lens through which we tackle challenges in interpretability, uncertainty quantification, continual learning, and other real-world deep learning systems.
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Our research explores how optimization principles can be extended and applied to address such challenges.
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<button class="fold-trigger" type="button" aria-expanded="false" aria-controls="applicationsDetail">
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<span class="fold-label">more</span><span class="fold-caret" aria-hidden="true">🔍</span>
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</button>
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</p>
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<div class="fold-detail" id="applicationsDetail">
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<div class="fold-item">
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<span class="fold-item-title">Interpretability <span class="paper-tag">(<a href="https://arxiv.org/abs/2505.16705" target="_blank">NeurIPS 2025</a>)</span></span>
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We bridge complex model architectures and human understanding, from uncovering model logic in realistic scenarios to evaluating the reliability of model outputs.
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</div>
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<div class="fold-item">
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<span class="fold-item-title">Continual Adaptation <span class="paper-tag">(<a href="https://arxiv.org/abs/2510.25798" target="_blank">NeurIPS 2025</a>)</span></span>
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We study constrained optimization problems where models adapt to new knowledge without disrupting what has already been learned, opening principled approaches to continual learning.
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</div>
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</div>
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</div>
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</div>
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<span class="orange">Advanced Optimization</span>
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Optimization has long been a source of crucial ideas that drastically enhance all corners of deep neural network training, and many of its most impactful questions remain open.
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Our research investigates these questions and develops optimization principles and algorithms for modern deep learning systems.
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<button class="fold-trigger" type="button" aria-expanded="false" aria-controls="optimizationDetail">
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<span class="fold-label">more</span><span class="fold-caret" aria-hidden="true">🔍</span>
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</button>
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</p>
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<div class="fold-detail" id="optimizationDetail">
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<div class="fold-item">
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<span class="fold-item-title">Optimization for Generalization</span>
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We study the role of loss landscape curvature in generalization and develop flatness-oriented optimization strategies with both theoretical advantages and scalability for modern deep neural networks.
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</div>
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<div class="fold-item">
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<span class="fold-item-title">Zeroth-Order Optimization for Black-Box Model Training</span>
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We develop methods that operate without direct gradient access, motivated by settings such as proprietary model APIs and decentralized learning systems where gradients are unavailable.
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</div>
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</div>
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</div>
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</div>
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‎research_focus/opt-compression.svg‎

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