Kiran Adhikari
Bioinformatics Scientist
I am Bioinformatics Scientist working on scalable genomics and multi-omics analysis pipeline in healthcare and biotechnology. I design and deploy production-grade NGS workflows for RNA-seq, whole-genome sequencing (WGS), metagenomics, Amplicon and DNA methylation analysis, using Docker, Nextflow and Argo across AWS, Kubernetes, and SLURM environments.
My day-to-day work spans pipeline automation, quality control, data wrangling and data integration, alongside applying machine learning, deep learning and statistical models for biomarker discovery, disease modeling, and multi-modal integration of transcriptomic, clinical, and imaging data.
I collaborate closely with biologists, clinicians, and engineers to deliver reproducible, well-documented analyses that translate complex sequencing data into actionable biological insight.
I enjoy working on biological problems where experimental complexity meets real-world problems which turn noisy, large-scale sequencing data into results. My work is driven by a strong sense of ownership and precision.
I care deeply about reproducibility, clarity, and building workflows that scale beyond a single project. I enjoy collaborating closely with wet-lab scientists, clinicians, and engineers, translating biological questions into computational solutions and ensuring the outputs are both scientifically sound and practically useful.
Over time, I have grown toward roles that emphasize responsibility, problem-solving, and stabilizing production pipelines, troubleshooting difficult datasets, or helping teams move from raw data to confident decisions.
I am motivated by my work because it contributes to meaningful scientific outcomes, challenges me to solve complex problems, and allows me to take ownership of the impact I create.
My day-to-day work spans pipeline automation, quality control, data wrangling and data integration, alongside applying machine learning, deep learning and statistical models for biomarker discovery, disease modeling, and multi-modal integration of transcriptomic, clinical, and imaging data.
I collaborate closely with biologists, clinicians, and engineers to deliver reproducible, well-documented analyses that translate complex sequencing data into actionable biological insight.
I enjoy working on biological problems where experimental complexity meets real-world problems which turn noisy, large-scale sequencing data into results. My work is driven by a strong sense of ownership and precision.
I care deeply about reproducibility, clarity, and building workflows that scale beyond a single project. I enjoy collaborating closely with wet-lab scientists, clinicians, and engineers, translating biological questions into computational solutions and ensuring the outputs are both scientifically sound and practically useful.
Over time, I have grown toward roles that emphasize responsibility, problem-solving, and stabilizing production pipelines, troubleshooting difficult datasets, or helping teams move from raw data to confident decisions.
I am motivated by my work because it contributes to meaningful scientific outcomes, challenges me to solve complex problems, and allows me to take ownership of the impact I create.
