Dr. Zihui Ma’s research focuses on leveraging AI-driven tools and big data to address critical challenges in natural hazards, urban resilience, and public health. She investigates how public responses to emergencies, such as wildfires, can be quantified using social media data and epidemic models like SIR, aiding decision-makers in optimizing disaster response strategies and enhancing urban resilience. Her research has been featured in Engineering at Maryland Magazine (“Social Media for Recovery and Action”) and Civil Remarks Magazine (“AI Can Help Sort Out Social Media Data During a Wildfire”). Dr. Ma’s recent interests have expanded to apply advanced AI techniques, including large language models (LLMs), to inform decision-making, climate adaptation, and sustainable development. Her current projects include using LLMs as world models for earthquake simulation and evaluation, investigating cascading disasters such as fire-induced landslides, and developing digital twins for large infrastructure systems.
She holds a Ph.D. in Civil Engineering from the University of Maryland and dual master’s degrees in Project Management and Structural/Seismic Engineering. She has authored 17 publications in high-impact journals and received prestigious awards, including the 2024 Arthur M. Wellington Prize and the 2024 Thomas Fitch Rowland Prize. Her interdisciplinary research bridges AI, social sciences, and engineering to advance disaster risk reduction, infrastructure resilience, and sustainable urban development. Dr. Ma actively collaborates with academic institutions, government agencies, and private sector partners to develop innovative emergency response and urban planning solutions.
Education
University of Maryland – 2024
Ph.D. in Civil Engineering
University of Maryland – 2020
M.S. in Civil Engineering (Project Management Concentration)
San Francisco State University – 2017
M.S. in Civil Engineering (Structure/Seismic Engineering Concentration)
San Francisco State University – 2015
B.S. in Civil Engineering
Publications
Ma, Z.*, Li, L., Mao, Y., Wang, Y., Patsy, O. G., Bensi, M. T., Hall, M. A., & Baecher, G. B. (2024). Surveying the use of social media data and natural language processing techniques to investigate natural disasters. Natural Hazards Review, vol. 25, no. 4, p. 03124003, Nov. 2024, doi: 10.1061/NHREFO.NHENG-2047.
Ma, Z.*, Li, L., Hemphill, L., Baecher, G. B., & Yuan, Y. (2024). Investigating disaster response for resilient communities through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season. Sustainable Cities and Society, 106, 105362. https://doi.org/10.1016/j.scs.2024.105362
Yu, H., Fan, L., Li, L., Zhou, J., Ma, Z., Xian, L., Hua, W., Zhang, Y., Gandhi, A., & Ma,X. (2024). Large language models in biomedical and health informatics: a bibliometric review. Journal of Biomedical and Health Informatics. https://doi.org/10.1007/s41666-024-00171-8
Fan, L., Li, L., Ma, Z., Lee, S., Yu, H., & Hemphill, L. (2024). A bibliometric review of large language models research from 2017 to 2023. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3664930
Erfani, A., Ma, Z., Cui, Q., & Baecher, G. B. (2023). Ex post project risk assessment: method and empirical study. Journal of Construction Engineering and Management, 149(2), 04022174. https://doi.org/10.1061/JCEMD4.COENG-12588 (received 2024 ASCE Best Paper)
Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., & Hemphill, L. (2023). ChatGPT in education: A discourse analysis of worries and concerns on social media. Education and Information Technologies, https://doi.org/10.1007/s10639-023-12256-9
Li, L., Mao, Y., Wang, Y. & Ma, Z. (2022). How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis. Journal of Air Transport Management, 102298. https://doi.org/10.1016/j.jairtraman.2022.102298
Li, L., Zhou, J., Ma, Z., Bensi, M. T., Hall, M. A., & Baecher, G. B. (2022). Dynamic assessment of the COVID-19 vaccine acceptance leveraging social media data. Journal of Biomedical Informatics, 129, 104054. https://doi.org/10.1016/j.jbi.2022.104054
Li, L., Ma, Z., Lee, H., & Lee, S. (2021). Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic? International Journal of Disaster Risk Reduction, 56, 102142. https://doi.org/10.1016/j.ijdrr.2021.102142
Li, L., Ma, Z., & Cao, T. (2021). Data-driven investigations of using social media to aid evacuations amid Western United States wildfire season. Fire Safety Journal, 126, 103480. https://doi.org/10.1016/j.firesaf.2021.103480
Li, L., Ma, Z., & Cao, T. (2020). Leveraging social media data to study the community resilience of New York City to 2019 power outage. International Journal of Disaster Risk Reduction, 51, 101776. https://doi.org/10.1016/j.ijdrr.2020.101776
