معرفی
Mostafa Mobasher is an Assistant Professor of Civil and Urban Engineering at New York University Abu Dhabi (NYUAD) with Associated Faculty affiliations in Civil and Urban Engineering and Mechanical and Aerospace Engineering at NYU Tandon School of Engineering.
Education:
- PhD in Civil Engineering & Engineering Mechanics, Columbia University (2017)
- MPhil in Civil Engineering & Engineering Mechanics, Columbia University (2017)
- MSc in Structural Engineering, Cairo University (2013)
- BSc in Civil Engineering, Loughborough University & The British University in Egypt (2011)
Research Interests: Dr. Mobasher leads the Computational Solid Mechanics (CSM) Lab focusing on fracture mechanics, multi-physical response modeling, and material behavior across scales for concrete, steel, polymers, ice, rock, and biomaterials. His work integrates Machine Learning and Artificial Intelligence with Finite Element Method for infrastructure resilience, energy systems, and aerospace applications. Current research emphasizes constitutive modeling, numerical algorithms, and multi-scale simulation of material failure under mechanical and environmental loading.
Scientific Awards:
- Sandooq AlWatan Award (2023)
- Thornton Tomasetti Ingenuity Award (2021)
- Thornton Tomasetti Ingenuity Award (2020)
- NSF Travel Award (2015)
- Best Student Award, British University in Egypt (2011)
Advising and Grants: Dr. Mobasher implements structured mentoring through published Mentee Guidelines. His research is funded by Sandooq Al Watan, NYUAD Institute, and industrial grants. As Co-PI and Energy Research Theme leader for the SHORES center (Sand Hazards and Opportunities for Resilience, Energy, and Sustainability), he directs interdisciplinary projects on energy resilience and material science applications.
Labs and Teams: The CSM Lab team (Diab Abueidda, Mareya Alkhoori, Yuqing Qiu, Bilal Ahmed, Fouad Amin, Panos Pantidis, Roshan Saji, Yijun Chen) develops high-fidelity failure models including VistaMat software. Current projects span fracture prediction in natural materials, ML-enhanced computational methods, and infrastructure integrity assessment for energy and transportation systems.
