박재우 교수

Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio-temporal Dirichlet process mixture model to analyze confirmed cases of COVID-19 in an urban environment. Our method can detect unobserved cluster centers of the epidemi

Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we prop

Long-term spaceflight induces multisystem stress, including cardiovascular deconditioning, skeletal muscle atrophy, immune suppression, and neuro-ocular syndromes. Current countermeasures reduce symptoms but cannot replicate the synergistic resilience needed for extended missions or critical illness. Hibernating animals, specifically brown bears (Ursus arctos), survive prolonged immobility, starvation, and bradycardia without resultant pathology. This review incorporates adaptations observed in

Abstract Wildfires are a major natural source of atmospheric aerosols, leading to air quality degradation and adverse human health effects. Accurate prediction of air quality effects from wildfires remains challenging due to uncertainties in fire emission estimates. To enhance the accuracy of fire emissions used in air quality forecast models, we developed a method that utilizes satellite aerosol optical depth (AOD) observations and air quality simulations to calculate dynamic emission scaling f

A large class of spatial models contains intractable normalizing functions, such as spatial lattice models, interaction spatial point processes, and social network models. Bayesian inference for such models is challenging since the resulting posterior distribution is doubly intractable. Although auxiliary variable MCMC (AVM) algorithms are known to be the most practical, they are computationally expensive due to the repeated auxiliary variable simulations. To address this, we propose delayed-acc

OBJECTIVE: The objective was to evaluate the trajectories of hematoma resolution and functional improvement after middle meningeal artery embolization (MMAE) for chronic subdural hematoma (cSDH), model the temporal pattern of cSDH resolution, and identify factors associated with favorable outcomes. METHODS: This real-world multicenter retrospective study included cSDH patients treated with MMAE at 24 centers between 2019 and 2024. Hematoma thickness was measured at baseline and at follow-up inte

Abstract Analysing crime events is crucial to understand crime dynamics, and it is largely helpful for constructing prevention policies. Point processes specified on linear networks can provide a more accurate description of crime incidents by considering the geometry of the city. We propose a spatio-temporal Dirichlet process (DP) mixture model on a linear network to analyse crime events in Valencia, Spain. We propose a Bayesian hierarchical model with a DP prior to automatically detect space-t

Abstract South Korea’s educational system has faced criticism for its lack of focus on critical thinking and creativity, resulting in high levels of stress and anxiety among students. As part of the government’s effort to improve the educational system, the innovation school system was introduced in 2009, which aims to develop students’ creativity as well as their non-cognitive skills. To better understand the differences between innovation and regular school systems in South Korea, we propose a