Kristopher Nolte

This paper develops an automated deep learning pipeline for Diptera wing landmark and semilandmark annotation, achieving high accuracy comparable to human observers and outperforming existing methods.

This paper evaluates using low-cost smartphone imaging with geometric wing morphometrics to accurately identify invasive Aedes mosquito species, finding it reliable but with higher observer variability compared to professional microscopes.

This study develops a CNN-based system for mosquito species identification using wing images, achieving high accuracy and demonstrating practical usability in real-world conditions.

The paper introduces a dataset of mosquito wing images to support geometric morphometry and machine learning for species identification.

The paper describes the rapid structural determination of SARS-CoV-2 proteins during the pandemic and the efforts to evaluate and improve these structures for drug and vaccine development.