- Design, development, and implementation of Bioinformatics and Machine Learning methods to integrate multi-modal genomics datasets to support clinical development- Developed and implemented bioinformatics pipelines, including gene-expression quantification (RNA-Seq), mutation calls (WES/WGS), detecting fusion transcripts, and ctDNA annotation for Omics data in AWS HealthOmics to support clinical development and facilitate the translation of clinical research- Implemented a user-friendly pipeline in Nextflow with best practices, supporting seamless updates and continuous improvements through flexible input parameters and container technologies like Docker and Singularity. Analyzed over 7000 clinical high-throughput sequencing samples- Benchmarked different bioinformatics tools and implemented the best processing and quality control (QC) practices for NGS analysis, integrating consensus approaches across various tools- Developed data-driven strategies to identify key QC metrics for reliable sample quality assessment, providing a more reliable approach than manual curation, and developed a machine learning (ML) framework for sample quality prediction that continuously improves as more data are processed- Established strategies to improve computational efficiency and achieve cost savings in processing NGS data in cloud computing environments- Provided guidelines for sequencing library preparation methods and recommendations for bioinformatics researchers in interpreting omics data analysis- Developed data visualization tools (Rshiny) for clinical data exploration

• Contributed to algorithms and eQTL detection pipelines to enable functional annotation of genetic associations to identify genetically-informed drug targets• Improved existing RNA-Seq analysis pipeline to quantify overlapping genes accurately and streamlined the workflow to reduce computational expenses• Worked on assessing lncRNAs as explanations of association signals and as potential drug targets• Worked on evaluating scRNA-seq eQTLs as a gene mapping source• Received 23andMe Therapeutics recognition award for reducing computational cost (> $40K) associated with reprocessing gene-expression datasets• Honored with two 23andMe Therapeutics Phenomenal Thanks awards for exceptional contributions in assisting colleagues

• Developed a homology-independent algorithm to analyze and query gene expression data across species. Characterized functionally important genes from cross-species gene expression datasets.• Developed bioinformatics pipelines to analyze RNA-Seq, ATAC-Seq, ChIP-Seq, WES, single-cell omics data, and other epigenetic next-generation sequencing data• Collaborated on projects studying the metastasis mechanisms of circulating tumor cells (CTCs)• Proficient in using bioinformatics tools for analyzing genomics datasets, including Bioconductor packages, GATK, BWA, STAR, Salmon, VEP, etc.• Extensive experience with biological databases: NCBI, Ensembl, GEO/ArrayExpress, SRA, GTEx, ENCODE, GDC, and TCGA

• Mentor, gtfBase project, Open Bioinformatics Foundation• Help and evaluate GSoC contributor participation• Development of curated resource of multispecies genomic regions

• Mentor, pysradb project, Open Bioinformatics Foundation• Development of a method to retrieve information from NCBI’s SRA database• Help and evaluate GSoC contributor participation

• Undergraduate Research Assistant at Computational Structural Biology Group, IIT Kharagpur• Computational prediction of microRNAs & Structural analysis of ribosomal complexes

Development of alignment-based filters for efficient classification of 16S rDNA sequences

Identifying the regulation of microRNA 5′ selection in fruit flies by analyzing small RNA sequencing data