Dr Milad Mousavi
Associate Lecturer
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Doctor of Philosophy (PhD) in Engineering and Construction Management, University of New South Wales, Australia, 2021–2025
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Master of Science in Construction Engineering and Management, Sharif University of Technology, Iran, 2018–2021
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Bachelor of Science in Civil Engineering and Environment, Sharif University of Technology, Iran, 2013–2018
Engineering
Civil and Environmental Engineering
Dr Milad Mousavi is an Associate Lecturer and Researcher in the School of Civil and Environmental Engineering at UNSW Sydney. He completed his PhD in Engineering and Construction Management at UNSW, where his research focused on developing data-driven and probabilistic decision-support systems for complex engineering environments. His expertise lies in artificial intelligence applications in construction and mining, construction digital twins, Bayesian Networks, adaptive deep learning, and IoT-based real-time risk monitoring. His work integrates sensor data, probabilistic modelling, and predictive analytics to support proactive safety and operational decision-making. Dr Mousavi has published in leading journals such as Reliability Engineering and System Safety and Automation in Construction, and actively contributes to research, teaching, and interdisciplinary initiatives at UNSW.
E-mail
Location
Civil Engineering Building (H20) Level 2, Room 207 Kensington Campus
- Publications
- Media
- Grants
- Awards
- Research Activities
- Engagement
- Teaching and Supervision
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Civil Engineering School Teaching Initiative Grant (STIG), UNSW (2025), AUD 11,225
Chief Investigator, AI-assisted moderation system for improving marking consistency in qualitative engineering assessments. -
International Seed Grant, UNSW (2023), AUD 5,000
PhD Researcher, Bayesian Networks and deep learning for IoT-based real-time underground mining safety risk management in collaboration with CCTEG.
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Best Presentation Award, 10th International Conference on Machine Learning Technologies (ICMLT), Helsinki, Finland, 2025
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Three Minute Thesis (3MT) Awards, UNSW, 2024, Runner-up in the UNSW Final and First Prize at the Faculty of Engineering, School of Civil and Environmental Engineering, and Centre for Infrastructure Engineering and Safety (CIES) Heats, including the People’s Choice Award
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Winner, Dr Tavakoli Award for Research Excellence, Sharif University of Technology, 2022
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Fully Funded TFS PhD Scholarship, UNSW, 2021–2025
My research focuses on the development of intelligent, data-driven decision-support systems for construction and underground environments, with a strong emphasis on safety, risk prediction, and digital transformation. I am actively involved in the following areas:
- AI-Driven Risk Modelling and Decision Support: Developing probabilistic and machine learning–based models, including Bayesian Networks and adaptive deep learning, to support real-time safety risk assessment and proactive decision-making in complex engineering systems.
- Construction and Underground Digital Twins: Designing data-driven digital twins that integrate IoT sensor data, probabilistic models, and predictive analytics to enable continuous monitoring, forecasting, and scenario analysis in underground mining and construction contexts.
- IoT-Based Real-Time Monitoring and Analytics: Analysing multi-sensor data streams from operational environments to detect hazards, identify abnormal patterns, and improve situational awareness through interpretable and scalable analytics.
- Integration of BIM and Data-Driven Models: Exploring the integration of BIM with AI and probabilistic models to enhance system-level understanding, support operational planning, and improve resilience and safety management in infrastructure projects.