Lijing is a Ph.D. candidate in the Department of Geological Sciences at Stanford University. Her research focuses on using data-driven methods for efficient and sustainable groundwater exploration and exploitation. She is currently working on 1) geomodeling with electromagnetic images using computer vision methods and 2) hierarchical Bayesian uncertainty quantification method for reservoir predictions and further decision making. She is passionate about teaching data science methods to geoscience audiences and the broader scientific community.

Personal website:

Honors & Awards

  • SIAM Conference on Mathematical & Computational Issues in the Geosciences, Travel Award, Society for Industrial and Applied Mathematics (SIAM) (06/2021)
  • Stanford Data Science Scholars 2020-2022, Stanford Data Science Institute (09/2020)
  • GS Travel Fund Award 2021, Stanford University (10/2020)
  • Harriet Benson Fellowship Award, Stanford University (06/2020)
  • Second Prize and Best use of Planet Data, Stanford Big Earth Data Hackathon (04/2018)

Professional Affiliations and Activities

  • Student representative, Stanford Earth DEI (Diversity, Equity, Inclusion) Advisory Council (2021 - Present)
  • Graduate panelist, Stanford Earth IDEAL (Inclusion, Diversity, Equity,and Access) faculty search (2021 - 2021)
  • Co-President, Association of Chinese Students and Scholars at Stanford (2019 - 2020)
  • Student Organizing committee, Women in Data Science @ Stanford Earth (2019 - 2019)

Education & Certifications

  • B.S., Peking University, Applied Mathematics (2017)
  • B.S., Peking University, Space Physics (2017)

Work Experience

  • Data Science Intern, AI & Geosciences Program, Total E&P (6/15/2020 - 9/11/2020)

    AI & Geosciences Program, collaborating with Google Cloud Advanced Solutions Lab


    Sunnyvale, CA

  • Guest PhD Student, HydroGeophysics Group, Department of Geoscience, Aarhus University (6/12/2019 - 7/17/2019)



  • Teaching Assistant: GS 240 Data Science for Geoscience, Stanford University (1/1/2019 - 3/29/2019)



All Publications

  • Hierarchical Bayesian Inversion of Global Variables and Large-Scale Spatial Fields WATER RESOURCES RESEARCH Wang, L., Kitanidis, P. K., Caers, J. 2022; 58 (5)
  • Efficacy of Information in Mineral Exploration Drilling NATURAL RESOURCES RESEARCH Caers, J., Scheidt, C., Yin, Z., Wang, L., Mukerji, T., House, K. 2022
  • Global Sensitivity Analysis of a Reactive Transport Model for Mineral Scale Formation During Hydraulic Fracturing Environmental Engineering Science Li, Q., Wang, L., Perzan, Z., Caers, J., Brown Jr., G. E., Bargar, J. R., Maher, K. 2021

    View details for DOI 10.1089/ees.2020.0365

  • Quantifying the Effect of Precipitation on Landslide Hazard in Urbanized and Non-Urbanized Areas Geophysical Research Letters Johnston, E. C., Davenport, F. V., Wang, L., Caers, J. K., Muthukrishnan, S., Burke, M., Diffenbaugh, N. S. 2021; 48 (16)

    View details for DOI 10.1029/2021GL094038

  • Probabilistic Evaluation of Geoscientific Hypotheses with Geophysical Data: Application to Electrical Resistivity Imaging of a Fractured Bedrock Zone Journal of Geophysical Research: Solid Earth Miltenberger, A., Uhlemann, S., Mukerji, T., Dafflon, B., Williams, K., Wang, L., Wainwright, H. 2021; 126

    View details for DOI 10.1029/2021JB021767

  • Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods CVPR 2019 Rustowicz, R. M., Cheong, R., Wang, L., Ermon, S., Burke, M., Lobell, D. B. 2019