Brad Balderson
Research Fellow at UQ Institute for Molecular Bioscience
Queensland, Australia
Summary
👤
Senior🎓
Top SchoolBrad Balderson is a research-focused bioinformatician with nine years’ experience developing and implementing computational methods across single-cell, spatial transcriptomics, and population genomics. Currently at the UQ Institute for Molecular Bioscience, he builds and benchmarks sequence-to-function models to map causal variants underlying complex traits, having previously driven transcriptomics–epigenetics and addiction-related work at the Salk Institute. He co-developed tools such as Cytocipher and stLearn CCI to resolve granular cell subtypes and infer cell–cell communication in spatial data, and applies machine learning to link DNA variation with epigenetic effects. Trained with a PhD in Biomathematics/Bioinformatics from The University of Queensland, he combines rigorous method development with hands-on pipeline engineering across melanoma, breast and brain cancer, and developmental systems. Known for a pragmatic, curiosity-driven approach, he seeks to translate computational insight into experimental decisions and real-world impact.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD Biomathematics Bioinformatics and Computational Biology, Doctor of Philosophy - PhD Biomathematics Bioinformatics and Computational Biology at The University of Queensland
Github Skills (45)
spatial-transcriptomics10
rna-seq10
machine-learning9
variational-autoencoder9
single-cell-analysis9
genomics8
tsne8
sequencing8
autoencoder8
abaqus8
hierarchical8
single-cell-genomics7
bioinformatics7
sequence-alignment7
temporal-data7
Programming languages (8)
Github contributions (5)
BradBalderson/Cytocipher
Mar 2022 - Dec 2022
Analysis methods for analysing single cell RNA-seq data; particularly with the goal of checking if tentative clusters of cells are significantly different to one another in terms of their gene expression.
Contributions:4 releases, 369 commits, 178 pushes in 9 months
rna-seq
BiomedicalMachineLearning/stLearn
Apr 2021 - Nov 2022
A novel machine learning pipeline to analyse spatial transcriptomics data
Contributions:196 commits, 2 PRs, 135 pushes in 1 year 7 months
machine-learningspatial-transcriptomics
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