Uzezi E. Okinedo

Dynamic computational genomics researcher with extensive experience at UMass Boston, excelling in large-scale genomic data processing and workflow engineering. Proficient in Python, R, and high-performance computing, I have successfully developed automated pipelines and integrated multi-omics models, showcasing strong analytical skills and a commitment to impactful research.

• Whole-genome & low-pass sequencing QC + variant calling pipeline
• Multi-cohort GWAS + eQTL platform integrating millions of markers
• Network and causal modeling framework linking expression and phenotype

• Variant Analysis & Statistical Genetics
• Large-Scale Genomic Data Processing (WGS/WES/RNA-seq)
• Workflow Engineering: HPC (SLURM), Terra, WDL, Nextflow
• Programming: Python, R, Bash, Linux, Git
• QC Pipelines & Reporting
• Multi-omics Integration & Interpretation

• Graduate Researcher – Computational Genomics, UMass Boston, 08/21–Present, Processed high-volume sequencing datasets; performed variant calling, GWAS, and eQTL., Developed automated Python/R/Bash workflows for QC, annotation, and association testing., Built scalable HPC pipelines using SLURM., Conducted network-based inference, mediation analysis, and integrative multi-omics modeling.
• Research Scholar – Statistical Genetics, IRRI, 01/24–12/25, Linked genomic variation to biological traits using machine learning and statistical genetics., Built QC and association workflows across multi-country datasets.
• Assistant Lecturer & Researcher, University of Lagos, 08/18–Present, Taught computational biology, Python/R workflows, and supervised student research.