Plant genomics · bioinformatics · functional validation
Crop disease genomics for breeding.
I am a PhD plant genomics and bioinformatics scientist combining disease phenotyping, NGS and variant analysis, QTL/GWAS, CRISPR/Cas9 validation, diagnostic markers, and R/Python/SQL workflows. My current work extends into soybean cyst nematode resistance, dry bean host-pathogen genetics, AI-assisted image phenotyping, and breeder-facing data QA/QC.
Featured work
Research outputs with practical breeding value.
The strongest threads in my work are gene discovery, functional validation, marker development, AI phenotyping, and reproducible analytics for crop improvement.
Tsc2 / Exo70FX15 gene discovery with functional validation
Led wheat functional genomics work identifying Tsc2/Exo70FX15 as a causal tan spot susceptibility gene. The project connected 90K SNP genotyping, 43 SSR/KASP markers, high-resolution mapping in 7,002 F2 plants, EMS/TILLING, transgenic complementation, qRT-PCR, Sanger sequencing, and CRISPR/Cas9 knockout validation.
Breeder-facing analytics and data QA/QC
Built R Shiny dashboards and Python-supported QA/QC workflows for phenotypic data, outlier review, automated reporting, database handoff, validation logic, statistical modeling, genomic selection support, and reproducibility.
AI-assisted phenotyping for nematode resistance
Developing image-based workflows for soybean cyst nematode cyst and egg detection, with emphasis on label review, quantification, quality control, error analysis, and biological usability.
Markers, QTL/GWAS, and NGS pipelines
Developed and validated diagnostic markers and analyzed GBS, sequence QC, alignment, SNP discovery, variant calling, haplotypes, QTL, and GWAS to connect phenotype, genotype, and breeding action.
Experience
Experience across crop disease biology, genomics, and data systems.
Each role connects a biological problem with a method, a reproducible workflow, and an output that can support breeding or research decisions.
Postdoctoral Researcher
NDSU Plant Pathology, Microbiology & Biotechnology · Fargo, ND
- Develop data workflows for soybean cyst nematode resistance and dry bean host-pathogen genetics.
- Integrate phenotypes, genotypes, image-derived traits, and breeding metadata into usable analysis pipelines.
- Build AI-assisted phenotyping workflows for cyst and egg detection with label review, QC, and error analysis.
- Develop and validate diagnostic molecular markers for resistance screening and marker-assisted selection.
- Contribute to project planning, manuscripts, proposals, and graduate student mentoring.
Bioinformatics Data Scientist Intern
NDSU Agriculture Data Analytics · Fargo, ND
- Developed breeder-facing R Shiny dashboards for phenotypic data QA/QC and automated reporting.
- Supported workflows from raw data upload to database, validation logic, modeling, and genomic selection support.
- Contributed to MicroBIOM, linking records with images, genomic sequences, BLAST, pairwise alignment, and routine analyses.
- Improved performance-critical QA/QC functions with Python and documented reproducible handoffs.
Graduate Researcher
USDA-ARS and North Dakota State University
- Performed QTL mapping and GWAS for resistance trait discovery in wheat and triticale.
- Analyzed GBS/NGS data through QC, alignment, SNP discovery, and variant-calling workflows.
- Used mapping, comparative genomics, mutagenesis, expression analysis, and targeted gene modification to validate candidate genes.
- Developed diagnostic markers for breeding programs selecting disease-resistant germplasm.
Toolkit
Technical skills organized by the problems they solve.
Instead of a loose keyword list, this section shows where each tool fits: validation, sequence analysis, data systems, breeding decisions, and biological interpretation.
Functional genomics and editing validation
Susceptibility gene discovery, CRISPR/Cas9 knockout validation, target-region PCR/amplicon/Sanger sequencing, qRT-PCR, gene-expression analysis, EMS/TILLING mutants, and disease-response phenotyping.
NGS and genomic context
GBS, sequence QC, alignment, SNP discovery, variant calling, BLAST, pairwise alignment, haplotypes, gene annotation, and diagnostic marker validation.
Computational workflows
Reproducible scripting, dashboards, automated reporting, statistical analysis, visualization, and workflow documentation for teams that need traceable decisions.
Breeding and trait discovery
High-resolution mapping, QTL/GWAS, marker-assisted selection, germplasm screening, phenotype-genotype integration, disease-resistance genetics, and molecular marker development.
AI phenotyping
Machine learning, image processing, computer vision, label review, model QC, error analysis, and biological interpretation for high-throughput disease screening.
Structure-aware interpretation
AlphaFold/ColabFold-informed protein models, phylogeny, protein-domain interpretation, variant-effect reasoning, and clear communication of assumptions and limitations.
Papers
Searchable research record.
Search by gene, disease, crop, method, journal, year, or citation count. Start with Tsc2, CRISPR, QTL, GBS, tan spot, nematode, or marker.
Talks & posters
Conference outputs that show scientific communication.
Selected talks, invited presentations, and posters across PAG, NAPB, APS, cereal disease, genome editing, crop improvement, and data-science-facing work.
Recognition
Awards, leadership, and visible research momentum.
A quick snapshot of cited work, selected recognition, and activities that show communication and leadership beyond bench or code output.
Top cited publications
— citationsAwards & leadership
Contact
Talk crop disease genetics, bioinformatics, and functional validation.
Reach out for roles or collaborations aligned with plant genomics, bioinformatics, functional genomics, disease resistance, molecular marker development, genome editing validation, AI phenotyping, or breeder-facing data tools.