About Jeff Pu
Ge (Jeff) Pu is a postdoctoral research fellow dedicated to understanding water resource issues. His work so far has focused on integrating and deploying various innovative water resource sensors to understand environmental issues (i.e., Flooding, Erosion, and Harmful Algal Blooms).
Jeff Pu’s research interests and expertise lies at the intersection of environmental data science, hydrology, and environmental remote sensing. He specializes in applying advanced quantitative methods to monitor and model water systems, ranging from small streams to the Great Lakes.
Here is a summary of Jeff Pu's past research and expertise so far:
Geospatial Analysis & Environmental Remote Sensing
Google Earth Engine (GEE): Expert in leveraging cloud-based geospatial processing to analyze large-scale environmental changes over time.
High-Resolution Mapping: Skilled in using 1-meter resolution imagery to classify riparian vegetation and delineate river channels with high precision.
Geomorphology: Expertise in monitoring river channel migration, riparian buffer restoration, and physical changes in stream corridors using multi-temporal satellite data.
Hydrology & Water Resources
Watershed Monitoring: Experience in analyzing streamflow, solute fluxes (chemical transport in water), and water quality parameters.
Limnology (Lake Science): Recent specialization in large lake systems (Great Lakes), specifically regarding winter sampling campaigns and the integration of sensor data for "smart" lake monitoring.
Urban Hydraulics: Foundation in modeling non-stationary urban water systems related to stormwater and flood management.
Statistical Modeling & Uncertainty Quantification
Error Propagation: Specialized knowledge in quantifying how measurement errors spread through complex environmental models.
Monte Carlo Simulation: Proficiency in using Monte Carlo methods to estimate uncertainty in ecosystem studies (e.g., forest biomass and stream loads).
Data Integrity: Extensive experiences in assessing and improving uncertainty reporting in ecosystem studies, as well as environmental monitoring methods (in-situ and remote sensing).
Environmental Data Integration
IoT & Smart Monitoring: Emerging expertise in data acquisition systems, sharing frameworks, and the integration of Internet of Things (IoT) technologies for real-time water resource management.
Multi-Institutional Collaboration: Experience managing large, diverse datasets from collaborative projects like the "Great Lakes Winter Grab."
