Welcome

I'm Sriram P Chockalingam, a researcher in Computational Biology, specifically interested in the use of parallel computing in high-throughput sequencing applications and systems biology.

Work

I am working as a Research Scientist since 2016 at the Institute for Data Engineering and Science in Georiga Intitute of Technology at Atlanta, GA, USA. My research interests include Parallel Algorithms, High-Performance Computing and analysis of large-scale biological data specfically, problems related to high-throughput sequencing data and genome-scale gene networks. I completed by Ph.D in Computer Science and Engineering from IIT Bombay. Prior to my Ph.D, I was a software engineer and a consultant in an IT company.

Recent Publications

1. Bhattaram, Swethasree, Sriram Chockalingam, Maneesha Aluru and Srinivas Aluru (2026). ‘scSAGA: Single-cell Sampled Gromov Wasserstein Alignment for Scalable and Memory-efficient Integration of Multi-modal Single Cell Data’. In: bioRxiv. DOI: 10.64898/2026.03.26.714573.
2. Chockalingam, Sriram P, Maneesha Aluru and Srinivas Aluru (2025). ‘SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data’. In: Bioinformatics 41.2, btaf057. ISSN: 1367-4811. DOI: 10.1093/bioinformatics/btaf057.
3. Connolly, Erin, Tony Pan, Maneesha Aluru, Sriram Chockalingam, Vishal Dhere and Greg Gibson (2024). ‘Loss of immune cell identity with age inferred from large atlases of single cell transcriptomes’. In: Aging Cell 23.12. e14306 ACE-24-0600.R1, e14306. DOI: 10.1111/acel.14306.

For a complete list with co-authors and citations, please refer to the publications listed here or my Google Scholar profile.

Recent Software

Sope

Rust library for MPI . Based to the C++ library mxx, library provides a simplified, and type-safe bindings to common MPI operations with error handling. Also includes a collection of scalable, high-performance standard algorithms for parallel distributed memory architectures, such as sorting and distribution. Available at github.

SCEMENT

Software for scalable and memory efficient integration of large- scale single cell RNA-sequencing Data. Available at github.

For a complete list of software that I have authored/co-authored or advised or contributed to, please refer to the list here

Contact