eSupervisor Detail

About me

Bioinformatics researcher with 4+ years of academic experience in omics data analysis, molecular modeling, and dynamics. Strong foundation in genomics, transcriptomics, and systems biology with knowledge in Python, R, and Bash scripting for bioinformatics research.

Interests: My research interests lie at the intersection of plant stress genomics, systems and network biology, and machine learning–driven bioinformatics, with a long-term goal of developing computational frameworks that translate multi-omics data into actionable biological and agricultural insights. A central focus of my research is plant stress genomics, encompassing both biotic and abiotic stresses. I am particularly interested in understanding how crops respond at genomic, transcriptomic, and network levels to stresses such as pathogen infection, drought, salinity, and heat—challenges that are intensifying under global climate change. Building on my doctoral work on host–pathogen interactions in Arachis hypogaea against Aspergillus flavus, I aim to identify stress-responsive genes, pathways, and biomarkers that underlie resistance and tolerance mechanisms in crop plants. A second major pillar of my research is network biology and systems-level modeling. I seek to reconstruct and analyze gene regulatory networks, co-expression networks, and protein–protein interaction networks to uncover emergent properties of biological systems under stress conditions. By integrating multiple omics layers—genomics, transcriptomics, and proteomics—I aim to move beyond single-gene analyses toward systems-level interpretations of stress adaptation and resilience. The third core theme of my research is the application of artificial intelligence and machine learning in biology. I am interested in developing predictive and interpretable machine learning models to identify key regulatory nodes, stress-associated modules, and candidate genes from high-dimensional omics data. These approaches will be combined with graph-theoretic and dynamic modeling techniques to infer causal relationships and improve the predictive power of biological networks. A key outcome of this work will be the development of robust, reproducible, and open-source bioinformatics pipelines for stress genomics and network analysis. In parallel, my experience in human genomics and clinical sequencing analysis has shaped my computational rigor and workflow development skills. Although my primary research focus remains plant systems, this dual exposure strengthens my interest in cross-domain computational methodologies that can be adapted across biological contexts, reinforcing bioinformatics as a unifying discipline. Looking ahead, my translational research goal is to collaborate closely with plant breeders, molecular biologists, and data scientists to bridge the gap between computational discovery and practical agricultural applications. By integrating AI-driven predictions with experimental validation, I aim to contribute to the development of climate-resilient and disease-resistant crops, addressing critical issues of food security and sustainable agriculture. Overall, my research program is driven by the vision of building an interdisciplinary, data-intensive biosciences framework that combines plant genomics, systems biology, and machine learning to address biologically fundamental and socially relevant questions.