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Hyunsoo Cho
<p class="ql-align-justify">Professor Hyunsoo Cho is an assistant professor in the Department of Artificial Intelligence at Ewha Womans University. Prior to joining Ewha, he worked as a postdoctoral researcher at Seoul National University and as a visiting researcher at NAVER, where he participated in the HyperCLOVA X project. He received his Ph.D. in Computer Science and Engineering from Seoul National University in 2023. His research focuses on natural language processing (NLP) and machine learning. In particular, his work explores foundation models, anomaly detection, and self-supervised learning. His previous research includes alignment learning methods such as instruction tuning and reinforcement learning from human feedback (RLHF), improving the reasoning capabilities of large language models, response-level uncertainty estimation, and knowledge interaction between parametric and non-parametric memory systems. More recently, his research has expanded to topics such as permanent unlearning in large language models, robustness to distribution shifts, and reliable operation of retrieval-augmented generation systems. He also serves as a reviewer and program committee member for major international conferences including ACL, EMNLP, NAACL, and AAAI.</p>
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Kwan Woo Nam
<p class="ql-align-justify">Professor Kwan Woo Nam conducts research aimed at innovating the paradigm of next-generation energy materials development through an autonomous laboratory that integrates artificial intelligence and robotics. As a leading expert in the design and mechanistic study of next-generation secondary battery materials, his research focuses on overcoming the limitations of current energy storage systems using advanced functional materials, with a particular emphasis on Metal–Organic Frameworks (MOFs).</p><p class="ql-align-justify"><br></p><p class="ql-align-justify">In particular, he is spearheading the establishment of the “IMMS Autonomous Laboratory,” which combines AI-driven data pipelines with robotics to maximize efficiency in the discovery of novel materials and the optimization of experimental conditions. By integrating machine learning (ML) algorithms with mobile robotic systems, his group has developed an intelligent research platform capable of 24/7 autonomous operation, enabling high-throughput screening and accelerated discovery of high-performance materials.</p><p class="ql-align-justify"><br></p><p class="ql-align-justify">His main research areas include next-generation secondary battery systems and battery interface engineering. His work involves developing high-efficiency and high-stability electrode materials for advanced energy storage technologies such as all-solid-state batteries and sodium-ion batteries. In addition, he investigates the use of Metal–Organic Frameworks (MOFs), which possess uniform pore structures and highly tunable chemical properties, to design advanced materials that reduce interfacial resistance and enhance ionic conductivity—two key challenges in solid-state battery systems.</p><p class="ql-align-justify"><br></p><p class="ql-align-justify">Professor Nam is also actively involved in building a global autonomous experimentation network. Through close collaborations with leading autonomous laboratory groups in the United Kingdom—including the University of Liverpool, University College London (UCL), and Labman Automation—his research incorporates state-of-the-art automation infrastructure and AI-controlled experimental technologies. He also leads international collaborative research with institutions such as the University of Hong Kong (HKU) and the Fraunhofer Institute in Germany to develop MOF-based materials for next-generation solid-state batteries and related practical technologies.</p><p class="ql-align-justify"><br></p><p class="ql-align-justify">Furthermore, his research integrates fundamental knowledge in chemical and materials engineering with advanced data science to significantly accelerate the research and development timeline for battery materials, from synthesis to performance evaluation. Through these efforts, he aims to establish a new research paradigm for next-generation energy materials development.</p>
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Sookyung Kim
<p>Sookyung Kim is an Assistant Professor in the Department of Artificial Intelligence at Ewha Womans University, working at the intersection of reinforcement learning, large language model agents, and AI for Science. Her research focuses on building agent-based systems for automated scientific discovery, integrating hypothesis generation, simulation, multi-agent critique, and uncertainty-aware decision-making into closed-loop discovery pipelines. She previously held research positions at Lawrence Livermore National Laboratory (LLNL) and at SRI International, including its former research division Palo Alto Research Center (PARC), where she led projects in climate modeling, drug discovery, novelty-aware agents, and reinforcement learning–based control systems under major U.S. research programs. She has published in leading machine learning venues including ICML, ICLR, NeurIPS, ACL, and UAI, bridging core machine learning methods with scientific applications. She has extensive experience organizing scientific meetings at the ML–science interface, having served as Program Chair and Organizer of the Data Mining on Earth System Science (DMESS) workshop at ICDM (2018, 2019), organizer of “Big Data in the Geosciences” at AGU (2018), Local Chair of ACM CIKM 2025, and General Chair of the UN Women AI and Gender Conference 2025, and has also served as a U.S. Department of Energy Biological and Environmental Research (DOE BER) funding panel reviewer and reviewer for major venues including ICDM, CVPR, and ICLR workshops. </p>
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Won Bo Lee
<p class="ql-align-justify">Professor Won Bo Lee is a professor in the School of Chemical and Biological Engineering at Seoul National University. His research focuses on understanding how microscopic molecular structures and interactions determine the macroscopic properties and behavior of soft materials through statistical mechanics and computational modeling.</p><p class="ql-align-justify"><br></p><p class="ql-align-justify">Professor Lee develops and applies advanced computational methods to investigate complex molecular systems across multiple length and time scales. His research integrates molecular dynamics simulations, machine learning force fields (MLFF), density functional theory (DFT), and multiscale modeling to analyze the physical properties of soft materials, explore chemical reactions, and predict the behavior of novel materials. He also applies these computational approaches to the design and optimization of chemical reaction systems.</p><p class="ql-align-justify"><br></p><p class="ql-align-justify">Through these studies, Professor Lee aims to establish predictive computational frameworks that bridge molecular-scale phenomena and macroscopic material behavior, contributing to the rational design of advanced materials and next-generation chemical processes.</p>