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Ryan van der Heijden

PhD Student and Gund Doctoral Fellow

Ryan van der Heijden
Affiliated Department(s)

College of Engineering and Mathematical Sciences

Civil and Environmental Engineering

BIO

Ryan is a PhD student in Civil & Environmental Engineering, also pursuing a certificate of graduate study in Complex Systems and Data Science. Ryan’s recent research has explored ways to leverage remote sensing to evaluate risk associated with natural geo-hazards such as landslides and debris flows, especially following large forest fires. Ryan is also working with an interdisciplinary group using machine learning to evaluate predictors of childhood disease in Bangladesh.

Ryan focuses on the interaction between groundwater and streamflow with the goal of improving the national water model streamflow predictions.

Prior to ¶¶Òõ̽̽, Ryan worked for a geotechnical engineering firm in Boston where he was involved in forensic (post-failure) analyses, subsurface investigation programs, and dam rehabilitation designs.

Area(s) of expertise

Remote Sensing, Post-Wildfire Geo-Hazards, Slope Failure, Groundwater, Machine Learning, Roadways

Bio

Ryan is a PhD student in Civil & Environmental Engineering, also pursuing a certificate of graduate study in Complex Systems and Data Science. Ryan’s recent research has explored ways to leverage remote sensing to evaluate risk associated with natural geo-hazards such as landslides and debris flows, especially following large forest fires. Ryan is also working with an interdisciplinary group using machine learning to evaluate predictors of childhood disease in Bangladesh.

Ryan focuses on the interaction between groundwater and streamflow with the goal of improving the national water model streamflow predictions.

Prior to ¶¶Òõ̽̽, Ryan worked for a geotechnical engineering firm in Boston where he was involved in forensic (post-failure) analyses, subsurface investigation programs, and dam rehabilitation designs.

Areas of Expertise

Remote Sensing, Post-Wildfire Geo-Hazards, Slope Failure, Groundwater, Machine Learning, Roadways