Hi, I am Yichuan Zhu of Civil & Environmental Engineering Department at Temple University. I currently lead the Computational Geosystems Group that focuses on a variety of problems, including micro-mechanics of granular material, landslides characterization and mitigation, computational geosciences, and probabilistic machine learning and uncertainty quantification in geotechnical engineering.

Our research is inherently interdisciplinary, bridging the fields of Civil Engineering, Geosciences, and Computer Science. If you are interested in discussing research or joining my group, please send me email or check out our openings page for more information and the description of current openings.

Interests
  • Granular Mechanics
  • Remote Sensing & Applications
  • Uncertainty Quantification
  • Risk Assessment/Management
Experience

  • Assistant Professor, 2021-present

    Temple University

  • Post-doc Fellow, 2019-2021

    Kentucky Geological Survey

  • Ph.D. in Civil Engineering, 2014-2019

    Texas A&M University

Recent News

All news»

[06/2024]: Dr. Zhu togeher with Dr. Hui Wang from University of Dayton and Dr. Weibing Gong from Missouri University of Science and Technology chaired a session on Natural Hazard (MS 0706) during EMI/PMC 2024 Conference.

[03/2024]: Dr. Zhu presented the paper “Integration of Physical and Statistical Knowledge in Landslide Susceptibility Characterization” during Geo-Congress 2024 Conference at Vancouver, Canada. The work is based on Dr. Khabiri’s PhD dissertation.

[02/2024]: Welcome Habib as the new Ph.D. student to join CGG team.

[11/2023]: Dr. Zhu is orginizing a mini-symposium (MS 0706) titled “Natural Hazard Assessment with Monitoring, Modeling, and Uncertainty Quantification”, with Dr. Hui Wang from University of Dayton, and Dr. Weibing Gong from Missouri University of Science and Technology at the EMI 2024 Conference.

[08/2023]: Congratulations to Ph.D. candidate Sahand Khabiri successfully defended his dissertation entitled “Uncertainty Quantification of Landslide Susceptibility Mapping Using Bayesian Networks”.

Contact