currently: postdoc scientist (computational biology), earlham institute, uk (oct. 2025 - present) last seen working as: - phd student, john innes centre, uk (oct. 2021 - sep. 2025) - graphic design and web dev intern at iceland ocean cluster, iceland (mar. 2023 - may 2023) - bioinformatician, john innes centre, uk (jul. 2021 - sep. 2021)
i am a computational biologist passionate about using mathematical and computational frameworks to solve biological problems. i can write decent quality code, can train a machine learning model or two and wrangle large datasets. i am always eager learn more about cool techniques and computers in general. i can also work in the lab, without destroying everything, if required. when i am not doing science, i like to do graphic design and hike tall masses of land.
github: github.com/Gurpinder98 blueSky: @grpndr.bsky.social linkedin: linkedin.com/in/gurpinder-s-s-a75569268/
technical skills: bioinformatics (experience with sequencing data, transcriptomic data, time-series data), data analysis, machine learning (experience with tensorflow, gpflow, keras), web development (experience with flask, html, css, javascript, wordpress), git, hpc (experience with slurm, snakemake) programming and scripting: python (★★★★★), r (★★★★★), bash (★★★★☆), c (★★★☆☆), rust (★★☆☆☆) lab skills: plant biology (tissue sampling), molecular biology (rna isolation), microscopy, data collection
- regulation of flowering time within brassica napus. (phd project). i worked on inferring the first flowering time gene regulatory networks in brassica napus using time-series gene expression data. my work showed divergences in regulation of a key gene in a polyploid system. as part of this project, i have implemented gene regulatory network inference methods (see github), a reciprocal blast utility for gene mappings (see github) and helped in testing of a new curve registration method (see greatR). - a bayesian framework for differential gene expression. i collaborated with a team of researchers to develop a bayesian framework for ranking genes based on their statistical evidence for differential expression. i contributed to the development of the framework, the testing of method with available r packages (see github) and the writing of the manuscript. associated publications: 10.1101/2025.01.20.633909, 10.1101/2025.01.31.635902 and 10.48550/arXiv.2406.19989
for a list of my publications, visit google scholar.