연구
연구
연구실 소개
총 17건의 게시물이 있습니다.
There are a total of 17 posts.
- 지도교수Young-ri Choi
주요연구
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Research area: Computer Systems, System Software, Cloud computing, Machine learning platforms, Storage systems, Big data analytics platforms, High performance computing
The main goal of my research is to develop computer systems and system software technologies for supporting new classes of large-scale applications including machine learning and big data analytics efficiently on top of evolving hardware technologies such as accelerators and non-volatile memory.
Intelligent System Software Lab
- 지도교수Woongki Baek
주요연구
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Research area: System Software, Machine Learning Systems, Parallel and Distributed Computing, Computer Systems Security
Intelligent System Software Lab (ISSL) investigates innovative system software techniques that significantly improve the performance, efficiency, security, and reliability of computer systems. We take a vertically integrated research approach to maximize the synergistic effects across the entire computer system hierarchy including computer architecture, system software, runtimes, and applications. Currently, we focus on the following research projects – (1) system software for high-performance and efficient machine learning, (2) machine learning-augmented system software, (3) scalable and efficient parallel and distributed computing, (4) system software for large-scale and emerging memory systems, and (5) computer systems security.
Next-generation Networks and Systems Laboratory
- 지도교수Youngbin Im
주요연구
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Research area: Network System, Large-Scale System, Mobile System, Emerging Application, AI-based Networking/System
N2SL’s research focuses on building and deploying practical systems that span multiple disciplines, including network systems, wireless networks, operating systems, big data processing, and machine learning. Especially, we are working on the research themes including bridging theory and practice in system research, AI-based diagnosis and management of large-scale systems, supporting emerging applications in next-generation networks, new types of computing systems.
- 지도교수Antoine Vigneron
주요연구
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Research area: Computational geometry, algorithms design and analysis, computational complexity, experimental algorithmics
We conduct research in computational geometry, and we focus on the design and analysis of worst-case efficient algorithms for geometric problems.
- 지도교수Taesik Gong
주요연구
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Research area: Artificial Intelligence, On-Device AI, AI Personalization, Ubiquitous Computing
Our lab’s research is at the intersection of cutting-edge AI and its seamless integration into real-world applications, focusing on enhancing user experiences through intelligent, adaptive, and efficient on-device AI systems. We currently explore three primary research areas (but not limited to) that collectively aim to push the boundaries of AI technology, making it more human-centered, personalized, and resource-efficient: (1) Human-Centered AI Applications, (2) Adaptive and Personalized AI, and (3) Efficient On-Device AI Systems.
- 지도교수Seongil Wi
주요연구
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Research area: Computer Security, Web Security, Software Security, Browser Security
We conduct research and develop technologies aimed at enhancing the security of the universal interface—the Web. Our primary goal is to contribute to the global effort of ensuring the security of the world’s web by proactively detecting and reporting web threats. We achieve this through various cutting-edge technologies, including penetration testing, fuzz testing, static analysis, and artificial intelligence.
- 지도교수Seung-Hoon Na
주요연구
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Research area: Natural language processing
Our NLP lab’s ultimate goal is to build an Artificial General Language Intelligence (AGLI) based on System 2 thinking, which simulates human-like knowledge acquisition and manipulation. We aim to elevate large language models (LLMs) beyond their current System 1 capabilities of short-term recall to achieve deeper cognitive skills like long-term learning and conceptual understanding. Our current research focuses on solving inefficiencies in knowledge injection and manipulation through areas like knowledge editing, efficient reasoning, and progressive expansion via MoE. In the long term, we are dedicated to establishing the foundational technologies for next-generation language intelligence that can master knowledge at a human level.