Korea University · Medicine
Professor Sung Won Han's research lab specializes in statistical modeling and data analytics with a focus on real-world applications in healthcare surveillance, gene regulatory networks, corporate innovation, and spare parts demand forecasting. The lab develops advanced statistical and machine learning methods—such as CUSUM, EWMA, and adaptive Lasso—for detecting anomalies, modeling complex dependencies, and improving forecasting accuracy in high-dimensional or data-scarce environments. A recurring theme is the integration of theoretical rigor with practical industrial and public health challenges, particularly in handling discrete data, limited historical observations, and causal inference in observational data. The lab also emphasizes methodological innovation to support decision-making in manufacturing, logistics, and biomedical systems.
Figures are computed from collected data and may differ slightly.
Abstract Various control chart methods have been used in healthcare and public health surveillance to detect increases in the rates of diseases or their symptoms. Although the observations in many health surveillance applications are often discrete, few efforts have been made to ex‐ plore the behavior of detection methods in discrete distributions. Joner et al. ( Statist. Med . 2008; 27:2555–2575) investigated and compared the performance of the scan statistic methods with the cumulative sum (CU
Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed graphical models, where all the edges are directed edges and contain no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the acycl
Despite the importance of corporate foresight for innovation management, scholars have yet to identify the organisational processes through which corporate foresight influences a company’s innovativeness. Drawing on the resource-based view and dynamic capabilities theory, we developed a model and posited that the effect of corporate foresight on innovativeness is mediated by organisational learning, while the relationship between corporate foresight and organisational learning is moderated by in
Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations are newly recorded, and it helps to cope with inaccurate prediction caused by the insufficiency of historical observations. This study compared the properties of several exponen
A model for the steady-state wear behavior of polymer composite materials, including the effects of preferential load support by and surface accumulation of wear-resistant filler particles, is further developed. It is shown that the resultant inverse rule-of-mixtures description of steady-state composite wear rate behavior is independent of the assumed form of filler contact pressure, though preferential load support does affect the degree of surface accumulation of filler particles that occurs.
The proportion of the inventory range associated with spare parts is often considered in the industrial context. Therefore, even minor improvements in forecasting the demand for spare parts can lead to substantial cost savings. Despite notable research efforts, demand forecasting remains challenging, especially in areas with irregular demand patterns, such as military logistics. Thus, an advanced model for accurately forecasting this demand was developed in this study. The K-X tank is one of the
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