- MSc Electical and Computer Engineering - Research Assistant, ITI-CERTH - PhD Candidate CMS Chair, TUM

Research interests encompass deep learning applications, particularly in pedestrian dynamics, including both static and temporal predictions via CNNs, Transformers, recurrent networks and GNNs

Shaofan Wang‘s research focuses on reduced-order modeling, machine learning, computational fluid dynamics (CFD) simulation, heat and mass transfer, and sustainable building technologies. His work aims to develop efficient and accurate predictive models to enhance energy efficiency, optimize thermal management, and promote sustainable engineering solutions.

Mrs. Mengyan Peng is a Research Associate at the Institute of concrete structures at TU Dresden, specializing in AI-driven solutions for civil engineering. Her work focuses on integrating deep learning, OCR, and knowledge extraction to accelerate the analysis of 2D bridge drawings. She has authored multiple publications on civil engineering and computer vision. Her research aims to enhance efficiency, improve safety, and advance digitization within the civil engineering sector through cutting-edge machine learning and data analytics methods.

PhD student at the chair of Computational Modelling and Simulation (TUM). My primary research is artificial intelligence (AI), applied to construction drawings.

Omar Elsaka is a master’s student at Technical University of Munich in the Chair of Computational Modeling and Simulation. Their research focuses on sequential recommendation, dynamic graph neural networks, and building information modeling (BIM). They have worked on applying graph-based recommendation systems to predict user interactions in BIM authoring tools, with a focus on improving modeling efficiency.