Rate My Professor SangHyung Ahn

Dr. SangHyung Ahn is a Lecturer in the School of Civil Engineering at the University of Queensland. He holds a PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA, an MBA in international business from Hanyang University, South Korea, and a B.Sc. in Civil Engineering from Korea University, South Korea. Prior to his academic career, Ahn worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd., ranked among the world's top 15 international contractors by Engineering News-Record in 2015, from 2003 to 2007. He joined the University of Queensland in 2015 as a postdoctoral research fellow and began his role as Lecturer in 2017. Ahn coordinates and delivers undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522).

Ahn's research specializations include construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and construction management systems with Building Information Modelling (BIM). His broader interests cover construction operations modelling, simulation and visualisation; automation for construction operations analysis, data collection, and simulation modelling; scalable analytics (data mining and process mining); analytics presentation (information visualisation and 3D animation); sensing (remote sensing and multi-sensor systems); BIM; and decision making in complex systems and knowledge management. He has supervised PhD and Master's students to completion on topics such as BIM-LCA integration frameworks, vehicle trajectory analysis with UAVs and AI, and computer vision-based pedestrian trajectory analysis. Ahn secured funding for the 2020 project 'Establishing carbon credentials of timber in construction' from the Queensland Department of Agriculture and Fisheries. Key publications include 'Interpretable data-driven car-following modelling with adversarial inverse reinforcement learning' (2023), 'Car following modelling with constrained generative adversarial imitation learning' (2021), 'Drone-based vehicle identification: an empirical study of convolutional neural network performance' (2020), 'Agent-based simulation modeling of a Bus Rapid Transit (BRT) station using smart card data' (2018), 'RapidBridgeBuilder-simulation tool for accelerated bridge design and construction' (2015), and 'Process mining technique for automated simulation model generation using activity log data' (2015).