Bio

I am a Stanford Data Science Postdoctoral Scholar in the Department of Biology at Stanford University, supervised by Prof. Hunter Fraser. My research focuses on evolutionary dynamics and the development of high-performance computational tools to analyze complex biological systems. I earned my Ph.D. in Bioinformatics from the University of Calgary, Canada, where I investigated within-host evolution in pathogen genomics and cancer. Originally from Sri Lanka, I hold a First Class B.Sc. (Hons) in Biology from the University of Sri Jayewardenepura. I am passionate about advancing computational biology through the design and implementation of scalable software solutions that leverage GPU, CPU, and SSD architectures for large-scale genomic and evolutionary analysis.

Honors & Awards

  • Stanford Data Science Postdoctoral Fellowship, Stanford Data Science (2025-09-01)
  • Stanford Center for Computational, Evolutionary, and Human Genomics Postdoctoral Fellowship, Center for Computational, Evolutionary, and Human Genomics, Stanford University (2025-08-01)

Professional Education

  • Bachelor of Science, University Of Sri Jayewardenepura (2017)
  • Doctor of Philosophy, University of Calgary (2025)
  • Ph.D., University of Calgary, Canada, Bioinformatics (2025)
  • B.Sc., University of Sri Jayewardenepura, Sri Lanka, Biology/Biological Sciences, Honors (2018)

Stanford Advisors

Contact

  • Academic
    deshan@stanford.edu
    University - Scholar Department: Biology Position: Postdoctoral Scholar

Additional Info

Links

Research Interests

  • Data Sciences

All Publications

  • Apollo: a comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell NATURE COMMUNICATIONS Perera, D., Li, E., Gordon, P. M. K., van der Meer, F., Lynch, T., Gill, J., Church, D. L., de Koning, A., Huber, C. D., van Marle, G., Platt, A., Long, Q. 2025; 16 (1): 5783

    Abstract

    Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from HIV and SARS-CoV-2 cohorts derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.

    View details for DOI 10.1038/s41467-025-60988-8

    View details for Web of Science ID 001523451900035

    View details for PubMedID 40593638

    View details for PubMedCentralID PMC12219717

https://orcid.org/0000-0003-4654-0734