Xin Wei

3520 Green Court

Ann Arbor, MI 48105

Email: xincwei@umich.edu

I am currently a postdoctoral fellow at the University of Michigan, supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship funded by Schmidt Sciences. I earned my Ph.D. in Civil Engineering, specializing in Geotechnical Engineering, from Shanghai Jiao Tong University, including a period as a visiting scholar in structural engineering at the University of Illinois at Urbana–Champaign.

My research interests include risk, reliability, and resilience analysis of natural hazards, leveraging interdisciplinary approaches such as remote sensing, geospatial modeling, and both data-driven and physics-based methods. I am exploring integrated AI-physics solutions to understand the impact of past landslides on communities and to develop risk assessment tools aimed at mitigating future impacts.

News

Dec 21, 2025	2025 American Geophysical Union (AGU) Fall Meeting
Dec 15, 2025	AGU25 – Toward Reliable and Scalable Geohazard Intelligence
Nov 17, 2025	U-M Annual Data Science & AI Summit 2025
Sep 28, 2025	AI in Science Fellowship Entrepreneurial Workshop
Aug 04, 2025	U-M Knowledge-Guided Machine Learning (KGML) Workshop

Latest Posts

Oct 15, 2025	Geospatial Foundation Models
Sep 01, 2025	Knowledge-Guided Machine Learning (KGML)
May 01, 2025	Community-Curated, Open-Access Landslide Platform

Selected Publications/Presentations

Geosci. Front.

Improving pixel-based regional landslide susceptibility mapping

Xin Wei, Paolo Gardoni, Lulu Zhang, and 4 more authors

Geoscience Frontiers. Previously listed as an ESI Highly Cited Paper, 2024

Bib Link

@article{wei2024improving,
  title = {Improving pixel-based regional landslide susceptibility mapping},
  author = {Wei, Xin and Gardoni, Paolo and Zhang, Lulu and Tan, Li and Liu, Dongsheng and Du, Chunli and Li, Hui},
  journal = {Geoscience Frontiers},
  volume = {15},
  number = {4},
  pages = {101782},
  year = {2024},
  publisher = {Elsevier},
  doi = {10.1016/j.gsf.2024.101782},
  citations = {38},
}

Acta Geotech.

Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales

Xin Wei, Lulu Zhang, Paolo Gardoni, and 5 more authors

Acta Geotechnica, 2023

Bib Link

@article{wei2023comparison,
  title = {Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales},
  author = {Wei, Xin and Zhang, Lulu and Gardoni, Paolo and Chen, Yangming and Tan, Lin and Liu, Dongsheng and Du, Chunlan and Li, Hai},
  journal = {Acta Geotechnica},
  volume = {18},
  number = {8},
  pages = {4453--4476},
  year = {2023},
  publisher = {Springer},
  doi = {10.1007/s11440-023-01841-4},
  citations = {52}
}

Geosci. Front.

Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks

Xin Wei, Lulu Zhang, Hao-Qing Yang, and 2 more authors

Geoscience Frontiers. ESI Highly Cited Paper🏆, 2021

Bib Link

@article{wei2021machine,
  title = {Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks},
  author = {Wei, Xin and Zhang, Lulu and Yang, Hao-Qing and Zhang, Limin and Yao, Yang-Ping},
  journal = {Geoscience Frontiers},
  volume = {12},
  number = {1},
  pages = {453--467},
  year = {2021},
  publisher = {Elsevier},
  doi = {10.1016/j.gsf.2020.04.011},
  citations = {232},
}

Nat. Hazards

A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping

Xin Wei, Lulu Zhang, Junyao Luo, and 1 more author

Natural Hazards, 2021

Bib Link

@article{wei2021novel,
  title = {A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping},
  author = {Wei, Xin and Zhang, Lulu and Luo, Junyao and Liu, Dongsheng},
  journal = {Natural Hazards},
  volume = {109},
  pages = {471--497},
  year = {2021},
  publisher = {Springer},
  doi = {10.1007/s11069-021-04844-0},
  citations = {69}
}

AGU 2025

Unveiling the Underappreciated Consequences of Landslides across the United States with Generative AI

Xin Wei, Vaibhav Balloli, Lauren Palermo, Benjamin Mirus, Nathan J. Wood, Alice Pennaz, Aleeza Wilkins, Elizabeth Bondi-Kelly, and Sabine Loos

In AGU Fall Meeting 2025, Dec 2025

Selected for oral presentation

Bib Link

@inproceedings{wei2025agu,
  title = {Unveiling the Underappreciated Consequences of Landslides across the United States with Generative AI},
  author = {Wei, Xin and Balloli, Vaibhav and Palermo, Lauren and Mirus, Benjamin and Wood, Nathan J. and Pennaz, Alice and Wilkins, Aleeza and Bondi-Kelly, Elizabeth and Loos, Sabine},
  booktitle = {AGU Fall Meeting 2025},
  address = {New Orleans, LA, USA},
  month = dec,
  year = {2025},
  note = {Selected for oral presentation},
  organization = {American Geophysical Union},
  citations = {0},
  show_all_authors = {true}
}