Draft:Ali Mostafavi

Ali Mostafavi
Known forAI applications for disaster resilience; Disaster AI; Infrastructure resilience to extreme events
AwardsNational Science Foundation CAREER Award (2019)
Walter L. Huber Civil Engineering Research Prize (2025)
Academic background
Alma materPurdue University
Academic work
DisciplineCivil engineering
Sub-discipline
Infrastructure resilience to extreme events
InstitutionsTexas A&M University

Ali Mostafavi is an Iranian–American civil engineer, university professor, and technology entrepreneur. He holds the Zachry Professorship in Design and Construction Integration II in the Department of Civil and Environmental Engineering at Texas A&M University College of Engineering and directs the UrbanResilience.AI Lab. Mostafavi's research in disaster preparedness, mitigation, and recovery across urban infrastructure systems draws from machine learning, complex-systems theory, and artificial intelligence.

Early life and education

Mostafavi was born in Tehran and raised in Iran before enrolling in graduate studies in the United States. Details of his early education have not been publicly disclosed.

Academic career

Mostafavi holds the Zachry Professorship in Design and Construction Integration II in the Zachry Department of Civil and Environmental Engineering. In 2021, he became a Resilience Fellow of the 4TU Resilience Engineering Center in the Netherlands. In addition, he serves as the Director of Disaster AI Initiative Institute for Disaster Resilient Texas and is a faculty fellow of the Hazard Reduction and Recovery Center, Texas A&M University


Research and contributions

Early in his academic career, Mostafavi recognized that

Mostafavi's work integrates community-scale big data with advanced machine-learning models to map and forecast infrastructure and social vulnerability. Datasets are extracted from utility data, anonymized and aggregated cell phone movement data, Google Street View. 

The digital twin is a virtual model that mimics the structure and functionality of a real world system that is dynamically updated to inform emergency responders.[1]. By applying complex frameworks to datasets of aggregated cell phone movements or social media exchanges, models can inform first responders of the level of disaster response and recovery in real time.[2][3].
      • FLOOD PREDICTION***

[4]

[5]

[6]

[7]

[8]

[9]

      • COVID-19***


With his group, Mostafavi developed network‑level decision‑support tools now being adopted by the Texas Department of Transportation for statewide resilience planning, and methods accepted by the World Bank for road‑network risk assessments in Haiti, Colombia, and Guyana.

Research at Mostafavi's Urban Resilience AI lab produced an nationwide county-by-county index classifying vulnerability to power outage.[10]

Disaster AI tools

Mostafavi's UrbanResilience.AI Lab has released a family of analytics platforms—collectively branded Disaster AI—that provide near-real-time situational awareness for emergency managers and infrastructure owners. Several tools have are being piloted by agencies, such as the Texas Department of Transportation and the World Bank, to inform infrastructure-resilience planning and post-disaster response.

  • MaxFloodCast (2023) [11]
  • FloodDamageCast (2024) [12]
  • Elev-Vision (2024) [13]
  • FloodGenome (2024)[14]
  • Resili-Net (2023) [15]
  • FloodRisk-Net (2023) [16]
  • Network Contagion Flood Model (2020) [17]
  • Hybrid Deep-Learning Flood-Warning Model (2021) [18]
  • DeepCOVIDNet (2020) [19]
  • DAHiTrA (2022) [20]

Entrepreneurship

In 2022, Mostafavi founded Resilitix AI, a spin-off that commercializes his lab's AI-driven digital-twin platform for disaster management and situational awareness.[21] The startup has received an NSF SBIR Phase I award and a Texas A&M Innovation Award, and its technology was deployed during the 2024 Atlantic hurricane season.

Publications and metrics

  • More than 200 refereed journal articles; more than 350 total publications.
  • More than 8,400 Google Scholar citations; h-index = 50 (July 2025).
  • Ranked #181 of 226,271 civil-engineering scholars worldwide (top 0.08%) by ScholarGPS.

Honors and awards



Selected works

  • Lee, Cheng-Chun, et al. "Predicting Peak Inundation Depths with a Physics Informed Machine Learning Model." Scientific Reports, vol. 14, 2024, Article no. 14826. doi:10.1038/s41598-024-65570-8.
  • Liu, Chia-Fu, et al. "FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation." International Journal of Disaster Risk Reduction, vol. 114, 2024, Article 104971. doi:10.1016/j.ijdrr.2024.104971.
  • Ho, Yu-Hsuan, et al. "ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images." ACM Journal on Computing and Sustainable Societies, vol. 2, no. 2, 2024, pp. 1–18. doi:10.1145/3663671.
  • Liu, Chenyue, and Ali Mostafavi. "FloodGenome: Interpretable Machine Learning for Decoding Features Shaping Property Flood Risk Predisposition in Cities." Environmental Research: Infrastructure and Sustainability, vol. 5, no. 1, 2025, Article 015018. doi:10.1088/2634-4505/adb800.
  • Kaur, Navjot, et al. "Large-Scale Building Damage Assessment Using a Novel Hierarchical Transformer Architecture." Computer-Aided Civil and Infrastructure Engineering, 2024. doi:10.1111/mice.12981.
  • Yin, Kai, and Ali Mostafavi. "Deep Learning-driven Community Resilience Rating Based on Intertwined Socio-Technical Systems Features." 2023. arXiv:2311.01661.
  • Yin, Kai, and Ali Mostafavi. "Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas." 2023. arXiv:2309.14610.
  • Podesta, Cristian, et al. "Quantifying Community Resilience Based on Fluctuations in Visits to Points of Interest." Journal of The Royal Society Interface, vol. 18, no. 177, 2021, Article ID 20210158. doi:10.1098/rsif.2021.0158.
  • Dong, Shayan, et al. "A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness Using Channel Network Sensors Data." Computer-Aided Civil and Infrastructure Engineering, 2020. doi:10.1111/mice.12629.
  • Fan, Chao, Xiangqi Jiang, and Ali Mostafavi. "A Network Percolation-Based Contagion Model of Flood Propagation and Recession in Urban Road Networks." Scientific Reports, vol. 10, 2020, Article 1348. doi:10.1038/s41598-020-70524-x.
  • Ramchandani, Avinash, Chao Fan, and Ali Mostafavi. "DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions." IEEE Access, 2020. doi:10.1109/ACCESS.2020.3019989.
  • Fan, Chao, et al. "Disaster City Digital Twin: A Vision for Integrating Artificial and Human Intelligence for Disaster Management." International Journal of Information Management, 2019, Article 102049. doi:10.1016/j.ijinfomgt.2019.102049.

See also

References

  1. ^ Johnson, Eleanor (1 October 2024). "Digital twins beginning to deliver real-world benefits". National Science Foundation: Science Matters. Retrieved 4 March 2026.
  2. ^ "Big data-derived tool facilitates closer monitoring of recovery from natural disasters". National Science Foundation. 10 August 2021.
  3. ^ Zarley, B. David (21 April 2020). "Real-time data can save lives in a disaster". Free Think.
  4. ^ Early Start with Rahel Solomon (10 July 2025). "Texas Flood Warning System is Inadequate, Experts Warn, Live Interview on CNN in Early Start with Rahel Solomon". CNN.
  5. ^ Barca, Simona (26 July 2024). "Can AI help prevent flooding? A Texas A&M professor says it can". KXXV-TV.
  6. ^ Bullard, Justin (19 July 2024). "Can AI solve Houston flooding? Texas A&M researchers hope to find out". Houston Chronicle.
  7. ^ Opray, Max (21 November 2020). "COVID-19 and the bushfire season". The Saturday Paper.
  8. ^ "Hurricanes and wildfires are colliding with the COVID‑19 pandemic – and compounding the risks". The Conversation. 26 August 2020.
  9. ^ "Using AI and Big Data to predict the future spread of COVID-19 cases". Medical and Life Sciences News. 29 September 2020.
  10. ^ Gibbs, Alice (August 15, 2025). "Map Shows Where Power Outages Are Most Common in the US". Newsweek.
  11. ^ Lee, Cheng-Chun, et al. "Predicting Peak Inundation Depths with a Physics Informed Machine Learning Model." Scientific Reports, vol. 14, 2024, Article no. 14826. DOI: 10.1038/s41598-024-65570-8.
  12. ^ Liu, Chia-Fu, et al. "FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation." International Journal of Disaster Risk Reduction, vol. 114, 2024, Article 104971. DOI: 10.1016/j.ijdrr.2024.104971.
  13. ^ Ho, Yu-Hsuan, et al. "ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images." ACM Journal on Computing and Sustainable Societies, vol. 2, no. 2, 2024, pp. 1–18. DOI: 10.1145/3663671.
  14. ^ Liu, Chenyue, and Ali Mostafavi. "FloodGenome: Interpretable Machine Learning for Decoding Features Shaping Property Flood Risk Predisposition in Cities." Environmental Research: Infrastructure and Sustainability, vol. 5, no. 1, 2025, Article 015018. DOI: 10.1088/2634-4505/adb800.
  15. ^ Yin, Kai, and Ali Mostafavi. "Deep Learning-driven Community Resilience Rating Based on Intertwined Socio-Technical Systems Features." 2023. arXiv:2311.01661.
  16. ^ Yin, Kai, and Ali Mostafavi. "Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas." 2023. arXiv:2309.14610.
  17. ^ Fan, Chao, Xiangqi Jiang, and Ali Mostafavi. "A Network Percolation-Based Contagion Model of Flood Propagation and Recession in Urban Road Networks." Scientific Reports, vol. 10, 2020, Article 1348. DOI: 10.1038/s41598-020-70524-x.
  18. ^ Dong, Shayan, et al. "A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness Using Channel Network Sensors Data." Computer-Aided Civil and Infrastructure Engineering, 2020. DOI: 10.1111/mice.12629.
  19. ^ Chao Fan, and Ali Mostafavi. "An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions." IEEE Access, DOI: 10.1109/ACCESS.2020.3019989.
  20. ^ Kaur, Navjot, et al. "Large-Scale Building Damage Assessment Using a Novel Hierarchical Transformer Architecture." Computer-Aided Civil and Infrastructure Engineering, 2024. DOI: 10.1111/mice.12981.
  21. ^ Fan, Chao, et al. "Disaster City Digital Twin: A Vision for Integrating Artificial and Human Intelligence for Disaster Management." International Journal of Information Management, 2019, Article 102049. DOI: 10.1016/j.ijinfomgt.2019.102049.


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