I build data-driven methods for RNA-seq, Hi-C, and multi-omics integration with a focus on cancer biology and biomarker discovery.
I am a computational biology researcher focused on integrating transcriptomics, chromatin interaction data, and machine learning to answer clinically relevant questions.
My work sits at the intersection of bioinformatics, machine learning, and translational oncology. I focus on converting complex multi-omics datasets into interpretable models and decision-ready biological insights.
Machine learning pipelines for multi-omics disease profiling, transcriptomics, and network-based biological interpretation.
Python, R, Docker, TensorFlow, PyTorch, and reproducible bioinformatics workflows from preprocessing to modeling.
Developing ML models for Hi-C contact map prediction from RNA-seq and integrative analysis of cancer cohorts.
Gene-expression modeling experiments with regression-based analysis pipelines.
View ProjectNGS pipeline work tailored for microbial genomics and reproducible analysis.
View ProjectRNA-seq analysis workflow for streamlined transcriptomic processing and downstream interpretation.
View ProjectOngoing work toward robust modeling and tooling around Hi-C related computational tasks.
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