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 closely working in the programmes of MSc/BEng (Hons) Civil Engineering and Graduate Apprenticeship (GA)-BEng (Hons) Civil Engineering, while contributing to other BEng programmes. 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 degrees of BSc in Architectural Engineering (Summa Cum Laude, Ranked 1/61) from Hanyang University-ERICA and MSc in Architectural Engineering from Hanyang University, South Korea. He then earned his PhD degree (Construction Management Emphasis) 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, Member of the Korean Scientists and Engineers Association in the UK (KSEAUK), and Associate Member of the American Society of Civil Engineers (A.M.ASCE).

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 roadway improvement projects, through multi-contextual big-data-based machine/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 building information management techniques and quantitative/qualitative approaches into developing multi-criteria decision-support frameworks for improved AEC projects, ranging from design/construction to operational phases