Dr Junseo Bae has joined the School of Computing, Engineering and Physical Sciences at the University of the West of Scotland (UWS) since February 2018, as a Lecturer in Construction Management. He is involved in the programmes of BEng (Hons) Civil Engineering, Graduate Apprenticeship (GA)-BEng (Hons) Civil Engineering and MSc Construction Management with Digital Engineering (Programme Leader (Interim) since Sept 2019). In addition, he is a key member of Construction Innovation and Built Environment Research (CIBER) Group at UWS. Prior to joining UWS, Dr Bae served as a Visiting Lecturer and Instructor of Record in the Department of Construction Science at Texas A&M University-College Station in the United States.

Dr Bae received both BSc and MSc degrees from Hanyang University in South Korea. He then earned his PhD degree from Texas A&M University. His doctoral research was fully focused on creating, testing and validating a Machine-Learning-based spatiotemporal Decision-Support framework that learns and predicts the Mobility Impact of critical Highway Construction Work Zones by training Multi-Contextual Big Data.

• PhD Thesis: A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors
• MSc Dissertation: Reinforcement Steel Design applying Set-based Design Methodology to Flat Plate in the Structural BIM (S-BIM) Environment
• BSc Dissertation: Optimal Weight Design of Steel Structure followed by Selection of Member Section on Adaptive Simulated Annealing

Dr Bae is a Fellow of the Higher Education Academy (FHEA) in the UK.

Dr Bae's research is focused on big-data-driven decision-support modelling for streamlined construction planning, operation and management. His research topics include construction informatics, sustainability and asset management, improved project delivery and building information modelling (BIM). Strategic research agenda falls into four key areas that address the unique spectrum of challenges and issues facing councils, local authorities, public sector, affected communities and business enterprises:

Transport infrastructure informatics: Automating and predicting the level of mobility disruption and potential safety risks before and during construction for critical motorway improvement projects, through multi-contextual big-data-based machine learning or deep learning under arbitrary and user-defined “what-if” construction scenarios

Sustainability & asset management: Developing a state-of-art decision-support model through the integrated life-cycle assessment (LCA) and life-cycle cost analysis (LCCA), aiming at selecting the most sustainable infrastructure improvement project scenario alternative

Improved project delivery: Developing the optimal decision-support model through stochastic/statistical modelling and discrete-event simulations, which could predict time-cost-change order risks for different project classifications and market characteristics

BIM applications: Intermingling BIM techniques and numerical approaches into developing decision-support models for improved AEC projects, ranging from pre-construction to post-construction phases. 

Postgraduate Level

• Construction Business Economics (Module Coordinator (MC))
• Digital Engineering (MC)
• Research Design and Methods*
• MSc Dissertations

Undergraduate Level

• Supervision of Civil Engineering Honours Projects
• Modern Practice in Construction Management*
• Engineering Project Management (MC)
• Graduate Apprenticeship (GA)-Technical Communications (MC)
• Technical Communications (MC)
• Engineering Industry (MC)

*: Contributing to the module by lecturing certain topics