Ph.D. Student in Plant Genetics & Bioinformatics — developing adaptive ML pipelines for genomic prediction, constructing pangenomes, and characterizing organellar genomes in crop species.
I am a Ph.D. student in Plant Genetics & Bioinformatics at Texas A&M University, College Station, working at the intersection of computational genomics, machine learning, and plant biology. My research spans chloroplast and mitochondrial genomics, transcriptomics, pangenomics, and the development of scalable ML pipelines for crop improvement.
My doctoral work centers on two parallel tracks: Wheat Pangenome and evolution study — Assembling and analyzing pangenomes of Elite North American winter, spring, and durum wheat varieties using whole-genome resequencing. Uncovering selective sweeps, genomic diversity, and functional gene expansion across North American elite wheat germplasm — and GenoBridge, an ensemble ML framework for sample-size-adaptive genomic prediction integrated with GWAS-guided candidate gene discovery.
Before joining TAMU, I spent four years as a CSIR-NET Research Fellow at NIPGR, New Delhi, where I contributed to genomic and epigenomic studies in chickpea, ricebean, and rice, and administered the institute's HPC infrastructure. I also held a Research Assistant position at Montana State University, where I assembled wheat pangenomes and investigated cross-kingdom sRNA regulation.
I am a CIMMYT Academy affiliate (2024–2026) and hold competitive scholarships from TAMU including the Horticultural Sciences Graduate Scholarship and the Steidinger Citrus Scholarship.
Characterizing complete chloroplast and mitochondrial genomes of red-fleshed grapefruit cultivars (Rio Red, Ruby Red, Texas Red TR-1) via PacBio HiFi WGS. Investigating IR boundary polymorphisms and plastid-to-mitochondria gene transfer relevant to HLB disease.
Developing GenoBridge — a sample-size-adaptive ensemble ML framework integrating Ridge regression, Random Forest, XGBoost, MLP, and stacking ensembles for genotype-to-phenotype prediction. Introduces CV-R² diagnostics and ML-gated GWAS for improved statistical power allocation.
Assembling and analyzing pangenomes of Montana winter, spring, and durum wheat varieties using whole-genome resequencing. Uncovering selective sweeps, genomic diversity, and functional gene expansion across North American elite wheat germplasm.
Investigating microRNA-mediated cross-kingdom gene regulation between wheat and wheat stem sawfly (Cephus cinctus). Identifying host-derived sRNAs that may silence insect target genes as a novel mechanism of crop resistance.
Contributing to genome sequencing, DNA methylation landscape characterization, and transcriptome-wide association mapping in chickpea (Cicer arietinum) and ricebean (Vigna umbellata) at NIPGR, New Delhi.
RNA-seq transcriptomics of peanut seed-coat resistance to Aspergillus flavus. Identified 76 significant phenylpropanoid DEGs; highlighted UGT73C6 (+11.7 log₂FC) and a lignin-building cluster (BBE-like 13, Laccase-15, Dirigent-22) as primary resistance determinants.
Evolution of pathogenicity-associated genes in Rhizoctonia solani AG1-IA by genome duplication and transposon-mediated gene function alterations.
Sample-size-adaptive ensemble ML pipeline for genotype-to-phenotype prediction using SNP data from VCF files. Integrates Ridge regression, Random Forest, XGBoost, MLP, and stacking. Supports 30–100,000+ samples across any plant species. Distributed as standalone binaries — no Python required.
Hybrid GWAS + ML pipeline for candidate gene discovery with population structure correction. Takes ML prediction results from GenoBridge as input, applies ML-gated GWAS to focus statistical power on genomically predictable traits, and annotates candidate genes from GFF3.
Developed and deployed three institutional bioinformatics portals during tenure at NIPGR: RiceBean Portal (ricebeanportal.com), Chickpea Genome Browser (http://223.31.159.7/chickpea/), and Cicer Genome Resource (http://223.31.159.7/cicer/). Built with PHP, HTML/CSS, JavaScript, and MySQL.
Open to collaborations in plant genomics, ML pipelines & crop improvement.
I am always interested in discussing research collaborations, especially around genomic prediction frameworks, organellar genomics, pangenomics, and ML applications in plant breeding. Feel free to reach out via email or connect on GitHub and Google Scholar.