Senior Bioinformatician · Cancer Scientist · Open-source Methods Developer
Single-cell & spatial omics · Systems biology · Network science · Multi-omics · AI for biomedicine
I am a bioinformatician and computational biologist developing methods and software for extracting biologically meaningful structure from complex biomedical data.
My work sits at the intersection of single-cell and spatial omics, systems biology, graph-based modelling, cancer genomics, multi-omics integration and machine learning. A recurring theme across my research is turning high-dimensional molecular measurements into interpretable biological signals, whether that means discovering cell states, identifying influential network nodes, prioritising candidate drivers and biomarkers, or building practical tools that make these analyses easier to use.
I currently work on translational cancer bioinformatics in Melbourne, Australia, while continuing to develop open-source computational methods and research software.
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A marker-centric single-cell discovery ecosystem in R. CelliVerse connects ClustoCell clustering and sub-clustering, authentic marker discovery, cell-type annotation, large-dataset workflows and an optional natural-language Agent in one ecosystem.
Explore: |
Network influence analysis and experimental feature prioritisation in R.
Explore: |
| Project | What it is |
|---|---|
| CelliVerse-Project | Reproducibility resources, MarkerDB assets and analysis code supporting CelliVerse/ClustoCell research |
| AutoClone | R-based clonality analysis from colour-profile information for lineage-tracing experiments |
| My developments | Broader collection of graph-based methods, integrative models, R/Python software, Shiny applications and visualisation tools |
A marker-centric framework for discovering biologically informative major clusters, sub-clusters and cell states from single-cell data, with ranked marker programs linked directly to the inferred populations.
Experimental data-based Integrative Ranking combines experimental evidence, machine learning, network reconstruction and influence ranking to classify and prioritise candidate drivers, biomarkers and mediators.
Integrated Value of Influence (IVI) integrates complementary topological dimensions to identify influential network nodes. SIRIR provides a simulation-based route for evaluating spreading influence.
single-cell & spatial omics
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├── cell-state discovery
├── marker identification & annotation
├── cancer genomics & tumour microenvironment
└── atlas-scale analysis
systems & network biology
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├── graph-based modelling
├── network influence
├── driver / biomarker prioritisation
└── computational perturbation
multi-omics & AI
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├── transcriptomics / proteomics
├── spatial transcriptomics / proteomics
├── machine learning
└── AI-assisted scientific workflows
The summary cards below are generated automatically inside this profile repository, so they do not depend on the rate-limited public github-readme-stats endpoint.
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ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner
iScience (2026) · DOI -
Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks
Patterns (2020) · DOI -
ClustoCell reveals cell states from single-cell transcriptomes
Current CelliVerse / ClustoCell research · CelliVerse
Publications, projects, talks and full research profile →
Bioinformatics · systems biology · single-cell & spatial omics · open-source scientific software