Lijing Wang

Assistant Professor

I am a geostatistician and hydrogeologist specializing in integrating process-based hydrologic modeling, machine learning methods, and multiscale datasets (e.g., geophysical surveys and in-situ hydrologic measurements) to understand groundwater-surface water interactions under climate and natural disturbances. Specifically, I am interested in the role of groundwater in sustaining mountain streams and thereby improving streamflow predictions. I am also interested in river restoration approaches such as beaver dams and beaver dam analogs, particularly in how they alter the water balance in southern New England and the western United States. I also develop machine learning and Bayesian inference methods to calibrate hydrologic models and quantify their uncertainty. Additionally, I’m committed to teaching data science to geoscientists, showcased in our new textbook, “Data Science for the Geosciences,” published by Cambridge University Press.

Education

• Ph.D.: Geological Sciences at Stanford University, minor in Computer Science
• B.S.: Space Physics and Applied Mathematics at Peking University

Publications

• Google Scholar