Building reproducible computational methods for genomics, precision medicine, and healthcare analytics.
Passionate about translating complex biomedical data into clinically meaningful insights using bioinformatics, machine learning, and statistical modeling.
I am a Computational Biologist with an integrated B.Tech + M.Tech in Pharmaceutical Engineering & Technology from IIT (BHU), Varanasi.
I develop computational pipelines that transform complex biomedical and healthcare data into meaningful biological and clinical insights through bioinformatics, machine learning, statistics, and data visualization.
My work spans genomics, transcriptomics, infectious disease modeling, healthcare analytics, and AI-driven biomedical research, with a strong focus on reproducible and scalable computational workflows.
🎓 Integrated B.Tech + M.Tech — IIT (BHU), Varanasi
🔬 Former Project Research Assistant — IIT Bombay
🇨🇦 Former Mitacs Globalink Research Intern — Western University, Canada
🧬 Interested in Computational Biology, Cancer Genomics & Precision Medicine
📍 Bangalore, India
💼 Open to Full-Time Opportunities
- Computational Biology
- Bioinformatics
- Cancer Genomics
- RNA-seq Analysis
- Single-cell RNA Sequencing
- Clinical Genomics
- Precision Medicine
- Infectious Disease Analytics
- Machine Learning for Healthcare
- AI in Biomedical Research
| Domain | Technologies |
|---|---|
| Bioinformatics | Seurat • DESeq2 • GATK • STAR • Cell Ranger • IGV |
| Workflow Management | Nextflow • Snakemake • Docker • Git |
| Machine Learning | Scikit-learn • SVM • Statistical Modeling |
| Visualization | Tableau • ggplot2 • Plotly • Matplotlib • R shiny |
| Databases | ClinVar • gnomAD • TCGA |
Python • GATK • Variant Annotation • Clinical Genomics
Developed a reproducible germline variant analysis pipeline for processing next-generation sequencing data, including variant calling, annotation, filtering, and interpretation using clinically relevant genomic databases.
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R • Seurat • Single-cell Transcriptomics
Performed single-cell RNA sequencing analysis to characterize tumor heterogeneity, identify marker genes, and investigate the tumor microenvironment. Developed computational workflows integrating transcriptomics and pathway analysis to prioritize therapeutic candidates for breast cancer.
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R • Bayesian Statistics • Spatial Epidemiology
Developed Bayesian disease mapping models to identify tuberculosis hotspots and quantify spatial risk patterns for public health decision-making.
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Python • Scikit-learn • Support Vector Machine (SVM) • Feature Engineering
Developed and evaluated machine learning models to predict neurotoxicity risk from biomedical data. Optimized feature engineering and model performance, achieving 83% classification accuracy using a Support Vector Machine (SVM), demonstrating the application of AI to predictive toxicology.
🔗
R • Tableau
Developed an end-to-end healthcare analytics platform for insurance claims analysis, fraud detection, settlement efficiency, and provider performance evaluation.
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📈
- Developed Bayesian spatial disease mapping models to identify tuberculosis hotspots.
- Integrated epidemiological, demographic, and geospatial datasets for public health analysis.
- Applied statistical modeling to support evidence-based disease surveillance.
- Developed computational workflows for breast cancer drug repurposing.
- Analyzed single-cell RNA sequencing datasets using Seurat.
- Performed transcriptomic and pathway analyses to identify therapeutic targets.
- 🧬 RNA-seq Analysis Pipelines
- 🤖 Machine Learning for Precision Medicine
- 📊 Healthcare Data Analytics
- 🧠 AI Applications in Computational Biology
- 📦 Reproducible Bioinformatics Workflows
"Transforming biomedical data into actionable insights through reproducible computational research."