Oieswarya Bhowmik

Computational biologist and high-performance computing researcher currently pursuing Ph.D. in Computer Science specializing in scalable genome assembly and NGS data analysis. Experienced in developing distributed, graph-based algorithms for large-scale bioinformatics workflows, optimizing data processing efficiency, and tackling challenges in genomic research. Passionate about leveraging computational methods to advance precision medicine, metagenomics, and structural variation analysis. Seeking opportunities to drive innovation at the intersection of computational biology, bioinformatics, and high-performance computing.

Graduate Research Assistant (Washington State University, Aug 2019 – Present)

• Developing scalable genome assembly workflows using third-generation and next-generation sequencing (NGS) data.
• Designing and implementing hybrid genome assembly pipelines and scaffolding tools leveraging sketching techniques such as minhash and minimizers.
• Optimizing graph-based reordering methods for long-read assembly using high-performance computing and distributed parallelism.

Research Assistant (National Taiwan University, Aug 2018 – May 2019)

• Developed an imputation web tool integrating Django and Snakemake for genomic data analysis.
• Conducted large-scale analysis of The Cancer Genome Atlas (TCGA) data, identifying key genomic patterns.
• Applied machine learning models to forensic SNP (Single Nucleotide Polymorphism) datasets for genetic analysis.
• Processed and analyzed metagenomic datasets (SILVA, Greengenes, ITS) using the QIIME bioinformatics pipeline.

• Bioinformatics
• Bioinformatics algorithm workflows
• Computational biology
• High performance computing
• Graph theory
• Data visualization
• Machine learning

• Tile-X: A vertex reordering approach for scalable long read assembly., Bhowmik O., Kalyanaraman A., Proc. 15th RECOMB Satellite Conference on Biological Sequence Analysis (RECOMB-SEQ), accepted/in press, 2025
• An Efficient Parallel Sketch-based Algorithmic Workflow for Mapping Long Reads., Rahman T., Bhowmik O., Kalyanaraman A., EEE/ACM Transactions on Computational Biology and Bioinformatics, 01, 114, 10.1109/TCBB.2024.3489478, 2024
• Maptcha: an efficient parallel workflow for hybrid genome scaffolding., Bhowmik, O., Rahman, T., Kalyanaraman, A., BMC Bioinformatics, 25, 263, 2024, 10.1186/s12859-024-05878-4
• An Efficient Parallel Sketch-based Algorithm for Mapping Long Reads to Contigs., Rahman, T., Bhowmik, O., Kalyanaraman, A., 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 157-166, 05/01/23
• Comparative Study on Selection of Genes Causing Alzheimer's Dementia Using Meta-heuristic Approaches., Bhowmik O., Murali M., International Journal of Pure and Applied Mathematics, 115, 8, 45-50, 2017

• First Place Winner, 3-Minute Thesis (3MT) Competition, School of Electrical Engineering and Computer Science, Washington State University (2025).
• Poster Presenter, Efficient and Scalable Graph-Theoretic Approach for Hybrid Scaffolding, 37th IEEE International Parallel and Distributed Processing Symposium (IPDPS), St. Petersburg, Florida, USA (May 15–19, 2023).
• Participant, International Conference on Recent Trends in Computing and Information Technology, Department of Computer Science and Engineering, SRM University (March 27–31, 2017).