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.

R / Python / SQL CRISPR validation QTL / GWAS AI phenotyping Diagnostic markers
Best fit
Plant genomics scientist Bioinformatics scientist Functional genomics researcher Genome editing validation Computational breeding support Molecular marker development
Publication entries
Conference outputs
Tracked citations
Output years

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.

Oct 2025 — Present

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.
Jun 2025 — Oct 2025

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.
Aug 2017 — May 2025

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.
2025PhD, Genomics, Phenomics & BioinformaticsNorth Dakota State University
2023Certificate, Big Data Applied StatisticsNorth Dakota State University
2020MS, Plant PathologyNorth Dakota State University
2017BSc, AgriculturePunjab Agricultural University

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.

CRISPR/Cas9qRT-PCRSangerEMS/TILLING

NGS and genomic context

GBS, sequence QC, alignment, SNP discovery, variant calling, BLAST, pairwise alignment, haplotypes, gene annotation, and diagnostic marker validation.

FastQCBowtie2SAMtoolsVCFtools

Computational workflows

Reproducible scripting, dashboards, automated reporting, statistical analysis, visualization, and workflow documentation for teams that need traceable decisions.

RPythonSQLR Shiny

Breeding and trait discovery

High-resolution mapping, QTL/GWAS, marker-assisted selection, germplasm screening, phenotype-genotype integration, disease-resistance genetics, and molecular marker development.

KASPPACEFAASAMAS

AI phenotyping

Machine learning, image processing, computer vision, label review, model QC, error analysis, and biological interpretation for high-throughput disease screening.

MLComputer visionImage QC

Structure-aware interpretation

AlphaFold/ColabFold-informed protein models, phylogeny, protein-domain interpretation, variant-effect reasoning, and clear communication of assumptions and limitations.

AlphaFoldColabFoldPhylogeny

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

— citations

Awards & leadership

Bayer B4U mentee2026
NAPB Borlaug Scholar Award2025
Genomics for Plant Pathologists Workshop co-organizer2025
NAPB George Washington Carver Scholar Award2024
NDSU GPB Outstanding Graduate Research Award2023 & 2024
Bayer Encompass Scholar2023

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.