Draft:Ali Mostafavi

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Ali Mostafavi
Born
Tehran, Iran
Citizenship Iran United States
Known forAI applications for disaster resilience; Disaster AI
AwardsNSF CAREER Award (2019) · Early-Career Fellowship, National Academies GRP (2021) · AWS Machine Learning Award (2022) · ASCE Daniel W. Halpin Award (2023) · ASCE Walter L. Huber Prize (2025)
Scientific career
FieldsDisaster AI; Urban resilience; Artificial intelligence
InstitutionsTexas A&M University
Websitemostafavi.engr.tamu.edu

Ali Mostafavi (Persian: علی مصطفوی) is an Iranian-American civil engineer, scholar, and technology entrepreneur. He holds the Zachry Endowed Professorship in the Department of Civil and Environmental Engineering at Texas A&M University and directs the UrbanResilience.AI Lab. Mostafavi is widely recognized for pioneering the use of artificial intelligence, machine learning, and complex-systems theory to improve disaster preparedness, mitigation, and recovery across urban infrastructure systems.

Early life and education

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

Academic career

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After joining Texas A&M in 2016, Mostafavi quickly advanced from assistant professor to a named professorship and, in 2021, became Resilience Fellow of the 4TU Resilience Engineering Center in the Netherlands. He leads multi-disciplinary projects with federal, state, and industry partners and has secured more than US$38 million in competitive research funding since arriving at Texas A&M.

Research and contributions

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Mostafavi's work integrates community-scale big data with advanced machine-learning models to map and forecast infrastructure and social vulnerability. His group has made significant contributions to research that pioneers theories, methods, and practices of civil infrastructure resilience to extreme events through advancing the state-of-the-art computational modeling and data-driven methods.

Disaster AI tools

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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:

  • MaxFloodCast (2023) – ensemble machine-learning system trained on hydrodynamic simulations that delivers block-level peak-inundation-depth forecasts within seconds, reducing physics-based model runtimes from hours to seconds for emergency routing and flood-plain management.[1]
  • FloodDamageCast (2024) – near-real-time flood-damage nowcasting framework that blends GAN-based data balancing with gradient-boosted trees to map residential property-damage severity at 500 m × 500 m resolution during unfolding events.[2]
  • Elev-Vision (2024) – computer-vision pipeline that segments Google Street View imagery to infer each structure's lowest-floor elevation (LFE), a key input for depth-damage and insurance models, without costly field surveys.[3]
  • FloodGenome (2024) – interpretable random-forest model (with SHAP explainability) that decodes the hydrologic, topographic and built-environment "DNA" governing parcel-level flood-risk predisposition across U.S. metro areas.[4]
  • Evac-Cast (2025) – location-intelligence analytics tool that ingests anonymized smartphone GPS data to track evacuation timing, rates and destinations in near real time, spotlighting compliance gaps and informing shelter logistics.[5]
  • Resili-Net (2023) – deep-learning framework for community-resilience rating that uses 12 socio-technical features to classify census areas into five resilience tiers and reveal feature importance for targeted capacity-building.[6]
  • FloodRisk-Net (2023) – unsupervised graph deep-learning model that captures spatial dependencies and nonlinear hazard–exposure–vulnerability interactions to assign emergent flood-risk levels across urban areas.[7]
  • Network Contagion Flood Model (2020) – SEIR-style differential-equation model that treats floodwater spread and recession in road networks like an epidemic, forecasting which road segments will inundate or reopen over time.[8]
  • Hybrid Deep-Learning Flood-Warning Model (2021) – CNN-LSTM architecture that fuses drainage-sensor telemetry with social-media inputs to issue short-horizon channel-network flood alerts and dashboards.[9]
  • DeepCOVIDNet (2020) – interpretable deep-learning model that combines mobility, socio-demographic and epidemiological signals to forecast county-level COVID-19 case growth seven days ahead and highlight key drivers.[10]
  • DAHiTrA (2022) – hierarchical-transformer network that automatically delineates building footprints and classifies post-disaster damage severity from high-resolution satellite imagery within minutes of image availability.[11]

Several of these tools have already been piloted by different agencies such as the Texas Department of Transportation and the World Bank to inform infrastructure-resilience planning and post-disaster response.

Entrepreneurship

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In 2022, Mostafavi founded Resilitix AI, a spin-out that commercializes his lab's AI-driven digital-twin platform for disaster management and situational awareness.[12] 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

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  • More than 200 refereed journal articles; more than 350 total publications.
  • More than 8,400 Google Scholar citations; h-index = 50 (July 2025).
  • Listed in the Stanford–Elsevier global top 2% scientists (Civil Engineering & AI; 2021–2023).
  • Ranked #181 of 226,271 civil-engineering scholars worldwide (top 0.08%) by ScholarGPS.

Example honours and awards

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  • ASCE Daniel W. Halpin Award for Scholarship in Construction (2023) – "…exceptional leadership in establishing an outstanding research program that pioneers theories and practices of civil-infrastructure resilience management to extreme weather events through advancing the state of the art in data-driven methods and computational modeling techniques."
  • NSF CAREER Award (2019).
  • Early-Career Fellowship, National Academies' Gulf Research Program (2021).
  • College of Engineering Excellence Faculty Award, Texas A&M University (2021).
  • AWS Machine Learning Award (2022).
  • Dean of Engineering Excellence Award – Associate Professor Level, Texas A&M University (2023).
  • Listed in the Stanford–Elsevier 'Top 2% Scientists' database for Civil Engineering and AI (2021–2023).
  • ASCE Walter L. Huber Civil Engineering Research Prize (2025) – "…exceptional leadership and significant contributions to research that pioneer theories, methods, and practices of civil-infrastructure resilience to extreme events through advancing the state-of-the-art computational modeling and data-driven methods."
  • Multiple Best Paper / Editor's Choice Awards, including Risk Analysis (2021) and ASCE Computing in Civil Engineering (2022).

Professional service

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Mostafavi serves on editorial boards of four ASCE journals, reviews proposals for major U.S. and international funding agencies, and frequently briefs National Academies panels on infrastructure resilience.

Selected works

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  • 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." arXiv preprint, 2023. doi:10.48550/arXiv.2311.01661.
  • Yin, Kai, and Ali Mostafavi. "Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas." arXiv preprint, 2023. doi:10.48550/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.

Media coverage

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  • Agan, Justin. "Putting AI on the Front Lines of Hurricane Preparedness." Texas A&M Engineering News, 3 June 2025.

See also

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References

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  1. ^ 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.
  2. ^ 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.
  3. ^ 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.
  4. ^ 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.
  5. ^ Agan, Justin. "Putting AI on the Front Lines of Hurricane Preparedness." Texas A&M Engineering News, 3 June 2025.
  6. ^ Yin, Kai, and Ali Mostafavi. "Deep Learning-driven Community Resilience Rating Based on Intertwined Socio-Technical Systems Features." arXiv preprint, 2023. DOI: 10.48550/arXiv.2311.01661.
  7. ^ Yin, Kai, and Ali Mostafavi. "Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas." arXiv preprint, 2023. DOI: 10.48550/arXiv.2309.14610.
  8. ^ 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.
  9. ^ 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.
  10. ^ 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.
  11. ^ 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.
  12. ^ 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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