Haojie Wang

E426, Computing and Data Science (CoDA)

389 Jane Stanford Way

Stanford, CA 94305

whj [at] stanford.edu

I am a Data Science Fellow at Stanford Data Science, Stanford University. I work with Professors Grant Miller, Pascal Geldsetzer, David Lobell, Marshall Burke, Stefano Ermon, Eran Bendavid and Gary Darmstadt.

I am interested in using insights from data science and remote sensing to address the challenges of sustainable development. I develop GeoAI approaches for detecting labor trafficking in supply chains, population health monitoring, natural hazard forecasting, and advancing understanding of how environmental risks interact with human health.

My doctoral research integrated machine learning with satellite imagery and multi-source geospatial big data to detect and forecast natural hazards under extreme events, assess their risk and develop mitigation strategies.

I obtained my Ph.D. in Civil Engineering from the Hong Kong University of Science and Technology, where I was advised by Professor Limin Zhang. Previously, I received my B. Eng. in Civil Engineering from China University of Geosciences.

news

Sep 03, 2025	I am happy to share that I have been awarded the Stanford Impact Labs Fellowship!
Jul 31, 2024	I am excited to receive the 2024 Best Paper Award of Engineering Geology, Elsevier.

selected publications

Acta Geotech.

Tunnel boring machine performance prediction using knowledge-driven transfer learning

X. Li, H. B. Li, H. J. Wang^*, and 2 more authors

Acta Geotechnica, 2025

* corresponding author

DOI Bib

@article{li2025tunnel,
  title = {Tunnel boring machine performance prediction using knowledge-driven transfer learning},
  author = {Li, X. and Li, H. B. and Wang, H. J. and Zhang, L. M. and Chen, Z. Y.},
  journal = {Acta Geotechnica},
  year = {2025},
  publisher = {Springer},
  doi = {10.1007/s11440-025-02656-1},
  dimensions = {true},
  note = {* corresponding author}
}

Gondwana Res.

Transfer learning improves landslide susceptibility assessment

H. J. Wang, L. Wang, and L. M. Zhang

Gondwana Research, 2023

🚀 Clarivate ESI Highly Cited Paper

ESI Highly Cited DOI Bib

🚀 Clarivate ESI Highly Cited Paper, top 1% cited in Geoscience

@article{wang2023transfer,
  title = {Transfer learning improves landslide susceptibility assessment},
  author = {Wang, H. J. and Wang, L. and Zhang, L. M.},
  journal = {Gondwana Research},
  doi = {10.1016/j.gr.2022.07.008},
  volume = {123},
  pages = {238--254},
  year = {2023},
  publisher = {Elsevier},
  dimensions = {true},
  note = {🚀 Clarivate ESI Highly Cited Paper},
}

Eng. Geol.

AI-powered landslide susceptibility assessment in Hong Kong

H. J. Wang, L. M. Zhang, H. Y. Luo, and 2 more authors

Engineering Geology, 2021

🏆 2024 Best Paper Award of Engineering Geology, 🔥 Clarivate ESI Hot Paper & 🚀 Highly Cited Paper

Best Paper Award DOI Bib

2024 Best Paper Award of Engineering Geology

@article{wang2021ai,
  title = {AI-powered landslide susceptibility assessment in Hong Kong},
  author = {Wang, H. J. and Zhang, L. M. and Luo, H. Y. and He, J. and Cheung, R. W. M.},
  journal = {Engineering Geology},
  volume = {288},
  pages = {106103},
  year = {2021},
  publisher = {Elsevier},
  doi = {10.1016/j.enggeo.2021.106103},
  dimensions = {true},
  note = {🏆 2024 Best Paper Award of Engineering Geology, 🔥 Clarivate ESI Hot Paper & 🚀 Highly Cited Paper},
}

GSF

Landslide identification using machine learning

H. J. Wang, L. M. Zhang, K. Yin, and 2 more authors

Geoscience Frontiers, 2021

🔥 Clarivate ESI Hot Paper & 🚀 Highly Cited Paper

ESI Hot & Highly Cited DOI Bib

🔥 Clarivate ESI Hot Paper & 🚀 Highly Cited Paper, top 0.1% cited in Geoscience

@article{wang2021landslide,
  title = {Landslide identification using machine learning},
  author = {Wang, H. J. and Zhang, L. M. and Yin, K. and Luo, H. Y. and Li, J. H.},
  journal = {Geoscience Frontiers},
  volume = {12},
  number = {1},
  doi = {10.1016/j.gsf.2020.02.012},
  pages = {351--364},
  year = {2021},
  publisher = {Elsevier},
  dimensions = {true},
  note = {🔥 Clarivate ESI Hot Paper & 🚀 Highly Cited Paper},
}