My research covers two significant areas in breeding: quantitative genomics and phenomics. I’m experienced in developing computational approaches to solve practical breeding problems. I’ve published iPat that fills the gap between complicated genomics software and the breeders. In the area of high-throughput phenotyping, I also released a Python pipeline, GRID, that automates the image segmentation process in field investigations. Currently, I’m working on video segmentation algorithm to track livestock behaviors without supervision from human inputs.

Keywords: Quantitative Genomics, Phenomics, Computer Vision, Hyperspectral Data, Software Engineering

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VTags: Unsupervised Object Tracking Algorithm

iPat: Friendly GUI for GWAS and GS
GRID: Hotspot-Searching algorithm