Geunwoo Kim
I am a Ph.D. student in Computer Science at UC Irvine, under the supervision of Michael Franz and Pierre Baldi.
I interned in Coupang's search team, where I enhanced the retrieval relevance via large-scale graph neural networks. Additionally, I worked closely with Dokyung Song on graph representation learning for binary code. Prior to this, I obtained my B.S. in Computer Science from Pohang University of Science and Technology, where I researched blockchain security with Jong Kim. In 2018, I also served as the president of the Postech Laboratory for Unix Security, a university hacking team. During my undergraduate break, I spent six months as a blockchain developer at Kodebox in Gangnam, Seoul, followed by another six months as a research assistant at the National University of Singapore. There, I worked with Min Suk Kang on blockchain network layer security.
Email  / 
Google Scholar  / 
LinkedIn  / 
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Research
My current research interests are centered on the development and application of LLM-based agents.
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Deep Learning Assisted Imaging Methods to Facilitate Access to Ophthalmic Telepathology
Geunwoo Kim*, Andrew W. Browne*, Anderson N. Vu, Josiah K. To, Don S. Minckler, Maria Del Valle Estopinal, Narsing A. Rao, Christine A. Curcio, Pierre F. Baldi
Ophthalmology Science 2023
We propose employing super-resolution imaging techniques, specifically utilizing the Denoising Diffusion Probabilistic Model (DDPM), to create high-fidelity pathology slide images. This approach offers a cost-effective alternative to advanced digital slide scanners for remote telepathology services using commonly available smartphones.
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Language Models can Solve Computer Tasks
Geunwoo Kim, Pierre Baldi, Stephen McAleer
NeurIPS 2023
AI & HCI workshop @ ICML 2023
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arXiv
A pre-trained large language model agent can execute computer tasks guided by natural language using a simple prompting scheme, recursively criticizing and improving its output.
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Improving Cross-Platform Binary Analysis using Representation Learning via Graph Alignment
Geunwoo Kim,
Sanghyun Hong,
Michael Franz,
Dokyung Song
International Symposium on Software Testing and Analysis (ISSTA) 2022
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paper
We propose XBA that uses a semisupervised approach to generate binary code embeddings which are aligned across platforms by collecting peripheral information of binary code using graph neural network.
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The Ticket Price Matters in Sharding Blockchain
Geunwoo Kim,
Michael Franz,
Jong Kim
International Workshop on Cryptocurrencies and Blockchain Technology (ESORICS workshop) 2022
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paper
We explore the impact of non-democratic environments on the security and scalability of blockchain sharding, proposes metrics to analyze the trade-off between security and scalability.
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