Hi, I’m Dr. Vivek Ruhela, a researcher passionate about unraveling the genetic and molecular architecture of complex diseases through the fusion of genetics, genomics, bioinformatics, and artificial intelligence.

I currently work as a Postdoctoral Research Scientist in the GiusTo Lab, Columbia University , where I investigate how genomic variation, gene- and pathway-level associations, and large-scale meta-analyses, powered by advanced sttistical and machine learning approaches to enhence our understanding of complex traits and neurodegenerative diseases. My work integrates whole-genome sequencing, multi-omics, and machine learning to identify hidden patterns of genetic influence across diverse populations.

Before joining Columbia, I was part of the SBILab (Signal Processing and Biomedical Imaging Lab) at the Indraprastha Institute of Informatino Technology, New Delhi, where I explored the intersection of genomics and artificial intelligence — developing bioinformatics workflows for RNA transcriptomic analysis, miRPipe, miRSim, in Chronic Lymphocytic Leukemia (CLL), and designing bio-inspired, AI-powered frameworks such as BDL-SP and BIO-DGI to decode the mutational landscape and identify key molecular drivers in Multiple Myeloma (MM). That cross-disciplinary foundation continues to guide how I connect biological intelligence and artificial intelligence to decode disease complexity and improve prediction and prevention strategies.

Research Interests

1. Genomics & Transcriptomics: Investigating how genomic variation and transcriptomic regulation contribute to complex traits and disease mechanisms across individuals and populations (e.g., miRPipe, miRSim).
2. AI & Machine Learning in Biology: Designing interpretable, biologically inspired AI frameworks for multi-omics integration, biomarker discovery, and precision medicine applications.
3. Bioinformatics & Computational Pipelines: Developing scalable, GPU-accelerated workflows for large-scale genomics, transcriptomics, and variant analysis—ensuring reproducibility and high-throughput performance.
4. Statistical & Population Genetics: Conducting genome-wide association, gene-based, and ancestry-informed analyses to uncover genetic determinants of complex diseases in diverse populations.
5. Epidemiological & Meta-Analytic Studies: Integrating multi-cohort data using robust statistical and AI-assisted meta-analysis to identify consistent genetic and environmental risk patterns.
6. AI-driven Systems Biology: Leveraging deep learning and network-based modeling (e.g., BIO-DGI, BDL-SP) to map disease pathways and interpret high-dimensional biological data.

Beyond analysis and coding, I enjoy creating visual narratives that translate complex biological insights into accessible knowledge — blending science, storytelling, and design.