S. V. Krishna Jagadish

This paper uses deep learning models to detect sorghum panicles from aerial and lab images, improving yield prediction through machine learning regression.

The paper develops a fully automated method using Faster R-CNN to detect root anatomical features in cross-section images and estimates key measurements with high accuracy.

This paper introduces ExRANGES, a method that improves gene regulatory network inference by incorporating rates of transcriptional change, showing better performance in identifying known regulator-target relationships across multiple species.