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  /  Github

My current research interests revolve around the development and application of efficient machine learning systems.

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.

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.

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.

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.