Faculty Research Profile

인공지능대학원

윤상웅

조교수Sangwoong Yoon

윤상웅

Sangwoong Yoon

Biography

학력

• Ph.D. in Mechanical Engineering, Seoul National University
• M.S. in Neuroscience, Seoul National University
• B.S. in Chemical and Biological Engineering, Seoul National University

주요 경력

• 2026-: Assistant Professor, Graduate School of AI, UNIST, Ulsan, Korea
• 2025-2026: Research Fellow, University College London, London, UK
• 2023-2025: Research Fellow, Korea Institute for Advanced Study,Seoul, Korea
• 2022: Applied Scientist Intern, Amazon, Seattle, US
• 2019: Research Scientist Intern, Kakao Brain, Pangyo, Korea
• 2016-2018: Machine Learning Team Lead, Haezoom Inc., Seoul, Korea\

수상/학회/외부활동

• 2025: NeurIPS 2025 Area Chair
• 2023: Outstanding Doctoral Dissertation Award at SNU ME
• 2021: Qualcomm Innovation Fellowship 2021 Winner

Research

Active Generative Agents Lab.

Active Generative Agents Lab.

Intelligent biological agents generate actions to explore the world actively. Artificially intelligent agents today attempt to mimic them, but they fall significantly behind in terms of efficiency, effectiveness, and reliability. The Active Generative Agents (AGA) Lab at the UNIST Graduate School of AI aims to investigate the principles underlying intelligence and apply them to build intelligent systems that can interact with the world actively, efficiently, and safely. AGA Lab is primarily interested in developing principled methods at the intersection of generative modeling and reinforcement learning. We apply our algorithms to various high-impact real-world applications, including, but not limited to, robotics foundation models, biochemistry, particle physics, chemical engineering, and adtech.

Intelligent biological agents generate actions to explore the world actively. Artificially intelligent agents today attempt to mimic them, but they fall significantly behind in terms of efficiency, effectiveness, and reliability. The Active Generative Agents (AGA) Lab at the UNIST Graduate School of AI aims to investigate the principles underlying intelligence and apply them to build intelligent systems that can interact with the world actively, efficiently, and safely. AGA Lab is primarily interested in developing principled methods at the intersection of generative modeling and reinforcement learning. We apply our algorithms to various high-impact real-world applications, including, but not limited to, robotics foundation models, biochemistry, particle physics, chemical engineering, and adtech.

연구분야

Natural Language Processing, Robotics, Machine-learning, Computer Vision

Natural Language Processing, Robotics, Machine-learning, Computer Vision