I study plant-pollinator interactions in temperate grasslands and develop AI-based tools for monitoring insect biodiversity at scale.
I am a PhD student in the Grozinger Lab at Penn State, working at the intersection of pollinator ecology, plant-pollinator interactions, and AI-based insect biomonitoring. My research spans field experiments at Konza Prairie Biological Station, controlled greenhouse studies, and the development and validation of computer vision tools for monitoring insect communities.
Within the NutNet global research network, I investigate how nutrient enrichment and altered disturbance regimes reshape grassland plant communities and the pollinators they support. In parallel, I help design and validate automated monitoring systems that lower the labor cost of measuring biodiversity at ecologically meaningful scales.
I am affiliated with Penn State's Center for Pollinator Research and the INSECT NET program, and I collaborate with researchers at Konza on long-term experiments in tallgrass prairie ecology.
Validating and deploying low-cost, computer-vision-based platforms that make continuous insect biodiversity data tractable for ecologists.
Continuous-deployment camera system for monitoring flying insect activity across landscape gradients and ecoregions.
Automated morphometric analysis of pinned and field-captured insects, calibrated against hand measurements across species.
Bee-focused identification and behavioral classification pipeline for pollinator-network research and applied conservation.
My work addresses how anthropogenic change reshapes interactions between flowering plants and the insects that depend on them, and how to measure these interactions at scales relevant to conservation.
Examining how N, P, and K addition in NutNet plots alters forb composition, floral rewards, and the structure of plant-pollinator networks at Konza Prairie.
Greenhouse experiments crossing nitrogen availability with simulated heatwaves in rapid-cycling Brassica to test how multiple stressors reshape flowering schedules.
Designing, validating, and deploying camera-based monitoring systems across ecoregions, with explicit attention to calibration, error structure, and ecological inference.
Quantifying nectar volume, sugar concentration, and pollen P:L ratios across forb communities to link bottom-up resource availability to bee foraging behavior.
A growing record of peer-reviewed research, preprints, and methodological contributions.
I'm always glad to hear from researchers working on pollinators, grassland ecology, or computer-vision approaches to biodiversity monitoring, and from students considering similar paths.