Skip to main content

CV

Professional summary

Muyu Gu works across experimental biology, R-based scientific data analysis, quantitative phenotyping, genetic association analysis, scientific visualization, and reproducible research documentation. His work has connected controlled biological experiments with image-derived measurements, statistical workflows, figures, interpretation, and research-facing tools.

Education

• PhD in Biological Sciences, South Dakota State University, 2025
• Master’s degree in Grass Science, Northwest A&F University, 2015
• Bachelor’s degree in Grassland Science, Northwest A&F University, 2012

Research and professional experience

Doctoral research, South Dakota State University | 2018-2025

• investigated physiological and genetic aspects of hydrotropism in the maize primary root;
• designed and conducted controlled plant experiments involving root responses to water and gravity;
• developed and applied a Hydrotropism Analyzer research system for quantitative root measurements;
• built R workflows for cleaning, analyzing, and visualizing experimental and genetic data;
• conducted genotype quality control, genome-wide association analysis, and candidate-region interpretation for quantitative maize traits;
• documented methods, assumptions, limitations, and interpretation in an English-language doctoral dissertation.

Undergraduate biology laboratory teaching | documented terms, 2018-2024

Taught laboratory sections in general biology, plant physiology, cell biology, genetics, and molecular biology at South Dakota State University.

Selected projects

• Hydrotropism research workflow: controlled experiments, quantitative phenotyping, R analysis, and genetic interpretation.
• Maize GWAS/QTL-oriented analysis: genotype quality control, multiple association models, linkage disequilibrium, and candidate-region analysis.
• Comparative root-response research: hydrotropism and gravitropism work involving maize, common bean, and additional plant species.
• MuyuWorks technical publishing workflow: Quarto, Markdown, lightweight CSS, local Git, link validation, and controlled factual sources.

Technical capabilities

Scientific data and modeling

R, RStudio, experimental-data analysis, data cleaning and reshaping, analysis of variance, mixed models, principal component analysis, correlation analysis, logistic regression, bootstrap methods, and multiple-testing correction.

Genetics and quantitative biology

Quantitative phenotyping, genotype quality control, GWAS/QTL-oriented analysis, TASSEL, GAPIT, population-structure and kinship considerations, linkage-disequilibrium analysis, and candidate-region interpretation within documented maize research.

Visualization and measurement

Publication-oriented scientific figures, multi-panel figures, correlation and genetic-analysis graphics, and image/video measurement with ImageJ, SMARTROOT, and IC Measure.

Reproducible computing and communication

Documented R workflows, Quarto, Markdown/QMD, Git, structured evidence management, English-language scientific writing, figure preparation, and undergraduate laboratory teaching.

Capability descriptions are scoped to the supplied research artifacts and this repository. They do not imply unsupported seniority or production-scale software engineering.

Selected publications and research outputs

• Gu, M. (2025). Physiological and Genetic Studies of Hydrotropism in the Maize Primary Root. Doctoral dissertation, South Dakota State University.
• Wang, Y., et al., including M. Gu. (2020). “Hydrotropism in the primary roots of maize.” New Phytologist, 226, 1796-1808. https://doi.org/10.1111/nph.16472
• Authored or co-authored research posters on root hydrotropism and gravitropism, quantitative phenotyping, candidate-gene analysis, and agricultural biological data.

This selected list is intentionally limited to records supported by the current source set. No total publication count or manuscript-status claim is made.