I am currently contributing in the Computational Genomics work at SCFBio, IIT Delhi. My research focuses on understanding genome structure and function using physicochemical DNA features, statistical modeling, and machine learning, with the broader aim of building interpretable and scalable genome annotation frameworks. I am involved in the development of Genome Reader, an all-in-one platform for physicochemical parameter-based annotation of eukaryotic genomes that integrates large-scale data curation, analysis, feature computation, and end-to-end workflows. In parallel, I am working on Prokaryotic Promoter Prediction, where I am adapting our DNA-level biophysical framework to identify promoter regions in bacterial genomes. I am also involved in the conceptualization of an in-house Ayurgenomics pipeline, developing a gene-to-phenotype-to-disease framework that integrates molecular features with constitution-based biological traits to generate hypotheses on disease susceptibility. Recently, I contributed to a large-scale biophysical profiling study that characterized over ~4.6 million genomic sites across multiple eukaryotic kingdoms, spanning coding sequences, promoters, enhancers, untranslated regions (UTRs), codons, and gene boundary elements including exon-intron junctions, gene starts, and gene ends, enabling physics-informed genome annotation. I also contributed to the development of ChemEXIN, a deep-learning-based exon-intron boundary prediction tool built on DNA physicochemical features, part of which originated from my master’s thesis work.