جست‌وجوی استاد و پژوهشگر

Evgeniya Dimova-Alexandrovaمشاهده پروفایل

دانشیار

• Residential Architecture
• Temporary Housing
• Sustainable Architecture
• +۵ مورد دیگر

Assoc. Prof. Dr. Evgeniya Dimova-Alexandrova serves as Deputy Dean for Academic Affairs at the Faculty of Architecture, University of Architecture, Civil Engineering and Geodesy (UACG) in Sofia, Bulgaria. She leads the Department of Residential Buildings and maintains an active research profile focused on innovative residential architecture solutions, with particular expertise in temporary habitation models and sustainable design approaches. Education PhD in Architecture of Buildings, Structures, Facilities and Details (2014), UACG Master's Degree in Architecture (2001), UACG Specialty in Construction (1995), UACG First English Language High School, Sofia (1994) Research Interests Dr. Dimova-Alexandrova specializes in modern and sustainable models of temporary habitation , with particular expertise in apartment buildings, villa settlements, camps, mobile homes, and resort complexes. Her research explores innovative approaches to space optimization in small residential units and disaster relief housing. Her work bridges architectural design with sustainable development principles, focusing on how residential architecture can adapt to changing social needs, tourism demands, and emergency situations. She has developed typological frameworks for temporary habitation along the Bulgarian Black Sea coast and investigated the potential of camping as a form of sustainable tourism that promotes social communication and environmental awareness. Publication Trends Dr. Dimova-Alexandrova's recent publications demonstrate a consistent focus on residential architecture solutions for temporary habitation across various contexts - from disaster relief to tourism development. Her work increasingly integrates sustainable design principles with practical spatial solutions, reflecting broader trends in architectural research toward environmentally conscious and adaptable housing models. Her research shows particular attention to Bulgarian architectural contexts while drawing on international examples, creating a valuable bridge between local needs and global architectural trends. The evolution of her work shows growing sophistication in addressing space constraints while maintaining habitation quality. Professional Activities Deputy Dean for Academic Affairs, Faculty of Architecture Division Editor, ANNUAL OF THE UNIVERSITY OF ARCHITECTURE, CIVIL ENGINEERING AND GEODESY Active researcher with publications in residential architecture and sustainable design Teaching and Mentoring Dr. Dimova-Alexandrova supervises student projects in regional planning, landscape architecture, urban planning, and the design of residential, public and industrial buildings. Her teaching approach emphasizes practical design skills combined with theoretical understanding of contemporary architectural challenges, particularly in the realm of temporary and sustainable habitation. Research Facilities Her work is supported by UACG's architectural design facilities and research infrastructure, enabling both theoretical research and practical design applications in residential architecture. She maintains connections with international research networks as evidenced by her co-authorship with scholars from multiple institutions.

Kijung Shinمشاهده پروفایل

دانشیار

• Data Mining
• Graph Algorithms
• Network Science
• +۴ مورد دیگر

Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.