جست‌وجوی استاد و پژوهشگر

• Geospatial Digital Twins
• Virtual Geographic Environments
• Remote Sensing
• +۷ مورد دیگر

Jun Zhu is a professor at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. His research is centered on geospatial digital twins, virtual geographic environments, and intelligent visualization for disaster risk management, with strong interdisciplinary work in AI, remote sensing, and VR-based simulation. His research interests include: Geospatial Digital Twins Virtual Geographic Environments AI for Remote Sensing 3D and VR-based Disaster Visualization Knowledge Graphs in GIS Public Risk Communication The recent articles (2023–2025) demonstrate a strong trend in integrating large language models, knowledge graphs, and deep learning with geospatial data to build intelligent, interactive, and immersive systems for infrastructure monitoring, disaster simulation, and public engagement. His work emphasizes data-knowledge fusion, human-centered visualization, and real-world applicability in urban and environmental contexts. Scientific Awards: No awards listed in the provided text. Advising and Grants: While Jun Zhu has extensive collaboration with researchers such as Weilian Li, Qing Zhu, Yakun Xie, and Jianbo Lai, and appears to lead research projects, there is no explicit mention of student advising, grant funding, or project titles in the provided data. Labs and Teams: Jun Zhu is likely part of a research group focused on digital twins and geospatial AI at Southwest Jiaotong University, collaborating closely with colleagues in geoinformatics and remote sensing, though specific lab names are not mentioned.

• Machine Learning
• Computational Modeling
• Biomedical Engineering
• +۳ مورد دیگر

Stephen Baek is a faculty member at the University of Iowa, Department of Industrial and System Engineering, with a PhD from Seoul National University's School of Mechanical and Aerospace Engineering. His research spans interdisciplinary applications of machine learning in computational modeling, biomedical engineering, and geometric data processing. Current Affiliation: University of Iowa, Department of Industrial and System Engineering PhD Institution: Seoul National University Stephen's work focuses on physics-informed machine learning , multiscale modeling , and geometric deep learning . He develops algorithms that integrate physical principles with neural networks for applications in energetic materials , human pose estimation , and medical imaging . His research also includes federated learning and interpretable AI for constraint-based synthesis and text classification. Recent publications highlight a physics-aware deep learning framework (PARCv2) for spatiotemporal dynamics, graph convolutional networks for airway mesh smoothing, and prototype trajectory methods for explainable AI. This work bridges computer science with applied physics and healthcare domains. Stephen actively collaborates across disciplines, evidenced by co-authors from institutions like Iowa, Seoul National University, and Samsung Electronics, with publications in venues such as CoRR , J. Mach. Learn. Res. , and NeurIPS . His methodological innovations address challenges in model heterogeneity , 3D surface processing , and constraint satisfaction .