About
Hi, I’m Dr. Vivek Ruhela, a computational genomics researcher investigating how genetic variation, molecular networks, and interpretable artificial intelligence can improve our understanding of complex diseases.
I am currently a Postdoctoral Research Scientist in the GiusTo Lab at Columbia University. My research integrates whole-genome sequencing, population genetics, transcriptomics, single-cell data, statistical association methods, and explainable AI to identify genes and pathways involved in complex traits and neurodegenerative diseases. A central goal of my work is to move beyond predictive black-box models and develop approaches that explain why particular genomic signals matter.
Research Focus
My work spans three connected areas:
- Genomics and population genetics: studying genetic variation, gene-level associations, ancestry-aware effects, and disease mechanisms across diverse populations.
- Multi-omics and bioinformatics: building reproducible computational workflows for genomic, transcriptomic, and single-cell analysis.
- Explainable AI and systems biology: designing biologically informed models that connect molecular features with genes, pathways, and interaction networks.
Selected Contributions
Before joining Columbia, I worked with the Signal Processing and Biomedical Imaging Lab at IIIT-Delhi. There, I developed open-source RNA-analysis tools including miRPipe and miRSim, and contributed to interpretable AI frameworks for cancer genomics.
These projects include BDL-SP, which investigates altered signaling pathways in multiple myeloma, and BIO-DGI, an attention-based graph-learning framework used to derive a 295-gene research panel for studying the transition from monoclonal gammopathy of undetermined significance to multiple myeloma.
Across these projects, my broader aim is to transform high-dimensional genomic data into biological explanations, reproducible tools, and testable hypotheses for precision medicine.
Science, Communication, and Collaboration
I also enjoy creating visual and accessible explanations of complex computational biology—connecting scientific analysis with storytelling, education, and open research.
I welcome conversations about collaborative research, genomics, population genetics, multi-omics, explainable AI, and bioinformatics tool development.