I decode complex biological systems through transcriptomics, multi-omics integration, and machine learning — building reproducible pipelines for biomedical discovery.
I'm a Computational Biologist & Bioinformatics Researcher with focused training in transcriptomics, multi-omics integration, and data-driven modeling of complex biological systems.
I design reproducible analysis pipelines and apply machine-learning methods to large-scale sequencing data — bulk and single-cell RNA-seq, metagenomics, and integrative systems biology. My research spans immune-mediated diseases, antimicrobial resistance, and infectious disease genomics.
I'm driven by hypothesis-led computational biology, methodological rigor, and the development of scalable, open, and interpretable analytical frameworks for biomedical research.
Hypothesis-driven, statistically rigorous, and biologically interpretable computational research at the intersection of omics and disease.
Immune-mediated and infectious diseases, with emphasis on gene-regulatory mechanisms underlying disease heterogeneity and progression.
Systems-level integration of bulk RNA-seq, single-cell transcriptomics, metagenomics, and metadata to infer biologically meaningful pathways and networks.
Computational modeling of antimicrobial resistance, including population-level genomic surveillance, transmission patterns, and evolutionary analysis.
Open, scalable, and interpretable computational methods for high-throughput biology — prioritizing statistical rigor and biological insight.
Selected publications and preprints — including first-author contributions in transcriptomics, AMR, and computational drug discovery.
Open to research collaborations, PhD opportunities, and interesting computational biology problems. The fastest way to reach me is by email.