Yonsei University · 工学
Professor Jongeun Choi's research lab specializes in intelligent sensing systems, spatio-temporal modeling, and machine learning for biomedical and physical systems. The lab develops self-organizing multi-agent systems that adaptively learn and predict complex spatio-temporal processes using noisy sensor data, with applications in healthcare, environmental monitoring, and biomechanics. A key focus is on interpretable artificial intelligence (XAI) for clinical decision support, particularly in osteoporosis risk screening, and on integrating physical priors—such as those from growth and remodeling models—into data-driven frameworks. The lab also explores biological regulation mechanisms in reproductive physiology, particularly autophagy in luteal cells, through computational and systems biology approaches.
Figures are computed from collected data and may differ slightly.
This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the max
The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.
This paper presents a novel class of self-organizing multi-agent systems that form a swarm and learn a spatio- temporal process through noisy measurements from neighbors for various global goals. The physical spatio-temporal process of interest is modeled by a spatio-temporal Gaussian process. Each agent maintains its own posterior predictive statistics of the Gaussian process based on measurements from neighbors. A set of biologically inspired navigation strategies are identified from the poste
Autophagy is known to be regulated by the phosphoinositide-3 kinase (PI3K)-protein kinase B (AKT) and/or mitogen-activated protein kinase 1/2 (MEK1/2)-extracellular signal-regulated kinase 1/2 (ERK1/2) pathways, leading to activation of mammalian target of rapamycin (mTOR), a major negative regulator of autophagy. However, some reports have also suggested that autophagic regulation by the PI3K-AKT and/or MEK1/2-ERK1/2 pathways may not be mediated by mTOR activity, and there is no direct evidence
Abdominal aortic aneurysms (AAAs) evolve over time, and the vertebral column, which acts as an external barrier, affects their biomechanical properties. Mechanical interaction between AAAs and the spine is believed to alter the geometry, wall stress distribution, and blood flow, although the degree of this interaction may depend on AAAs specific configurations. In this study, we use a growth and remodeling (G&R) model, which is able to trace alterations of the geometry, thus allowing us to compu
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