Conducts research at the intersection of landscape ecology, biodiversity science, and remote sensing
Uses spatial and trait-based data to study ecosystem persistence, shift, and recovery under disturbance
Employs LiDAR, drone imagery, and satellite time series to extract and model structural legacies of disturbance in forests
Integrates structural signals with ecological theory (e.g., size-abundance scaling, trait diversity frameworks) to understand ecosystem self-organization and stress responses
Develops computer vision pipelines to extract morphological traits from invertebrate specimens and combines them with environmental and disturbance data to test community assembly hypotheses
Background
Quantitative ecologist working at the intersection of remote sensing, biodiversity, and landscape ecology
Integrates ecology, data science, and remote sensing to understand ecosystem responses to change across scales
Focuses on interactions among forest structure, biodiversity, and disturbance history to explore ecological stability and resilience
Interested in trait variation within and across species, structural legacies of land use and disturbance, and spatial patterns emerging from environmental gradients
Strong advocate for open science, emphasizing transparent, scalable, and reproducible methods