Computational Geomorphology and Modeling Lab
Computational Geomorphology and Modeling Lab
Landscapes.
Data.
Discovery.
Geomorphology and data science, brought together to investigate environmental processes and change.
We connect field observations, spatial data, statistics, and computational models to ask how landscapes work—and what their patterns can tell us.
OBSERVE / ANALYZE / DISCOVER
From observations to understanding
Geomorphology Data science Statistical modeling Spatial analysis Field research
Our approach
Curiosity in the field. Insight in the data.
The Computational Geomorphology and Modeling Lab brings quantitative and statistical approaches to questions about environmental processes. Coastal and aeolian systems are a major research focus; data science, statistical reasoning, and reproducible computation are central to how we work.
We combine field measurements and high-resolution topography with inferential statistics, machine learning, and time-series analysis to examine patterns, test hypotheses, and evaluate uncertainty.
Research in motion
Different questions. Connected ways of thinking.
Explore the systems we study and the analytical approaches we bring to them.
01 / EARTH SYSTEMS
Landforms & sediment movement
Beach–dune morphodynamics, aeolian transport, and the environmental drivers of geomorphic change.
02 / DATA & MODELS
Patterns & predictions
Statistical modeling, spatial analysis, machine learning, and time series to understand complex environmental datasets.
03 / ENVIRONMENTAL APPLICATIONS
Habitats & natural infrastructure
Sea turtle nesting habitat and beach wrack research connecting environmental patterns with management questions.
Inside the workflow
How do we turn a question into understanding?
Open a method to explore how it fits into our research.
Learn. Collaborate. Investigate.
Your next question could start here.
We welcome motivated undergraduate and graduate students interested in geomorphology, data science, statistical modeling, and field-based environmental research.
Students build skills in data collection, coding, quantitative reasoning, and interpreting environmental datasets.
Curiosity
Bring your questions. Build your skills.
Field notebooks and code notebooks both belong here.