Donggyu Kim
Korea Advanced Institute of Science and Technology · Economics, Econometrics and Finance
About the Lab
Professor Donggyu Kim's research lab specializes in advanced optical imaging, surface wetting physics, and natural product chemistry. The lab develops innovative optical techniques—such as fiber bundle imaging with diffraction-limited resolution using digital micromirror devices—and explores fundamental wetting phenomena on nanostructured and rough surfaces, including wetting transparency of graphene and droplet-size-dependent wetting transitions. Additionally, the lab investigates bioactive natural products from marine microorganisms, focusing on structure elucidation and biological activity screening. These interdisciplinary efforts bridge photonics, materials science, and biochemistry to address challenges in medical imaging, surface engineering, and drug discovery.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Conventional wetting theories on rough surfaces with Wenzel, Cassie-Baxter, and Penetrate modes suggest the possibility of tuning the contact angle by adjusting the surface texture. Despite decades of intensive study, there are still many experimental results that are not well understood because conventional wetting theory, which assumes an infinite droplet size, has been used to explain measurements of finite-sized droplets. Here, we suggest a wetting theory applicable to a wide range of drople
These results suggest that the NMCT can pain relief, recovery from neck disability, ROM, and deep flexor endurance for patients with CR.
Since its discovery, the wetting transparency of graphene, the transmission of the substrate wetting property over graphene coating, has gained significant attention due to its versatility for potential applications. Yet, there have been debates on the interpretation and validity of the wetting transparency. Here, we present a theory taking two previously disregarded factors into account and elucidate the origin of the partial wetting transparency. We show that the liquid bulk modulus is crucial
Large volatility matrices are involved in many finance practices, and estimating large volatility matrices based on high-frequency financial data encounters the “curse of dimensionality”. It is a common approach to impose a sparsity assumption on the large volatility matrices to produce consistent volatility matrix estimators. However, due to the existence of common factors, assets are highly correlated with each other, and it is not reasonable to assume the volatility matrices are sparse in fin
In this article, to model risk contagion between the U.S. and China stock markets based on high-frequency financial data, we develop a novel continuous-time jump-diffusion process. For example, we consider three channels for volatility contagion—such as integrated volatility, positive jump variation, and negative jump variation—and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and estab
The existing estimation methods for the model parameters of the unified GARCH–Itô model (Kim and Wang, ) require long period observations to obtain the consistency. However, in practice, it is hard to believe that the structure of a stock price is stable during such a long period. In this article, we introduce an estimation method for the model parameters based on the high‐frequency financial data with a finite observation period. In particular, we establish a quasi‐likelihood function for daily
Research Areas
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