Integrating bulk and single-cell multi-omics to decode gene regulatory programs in chronic lung disease.
PhD in Computational Biology · Helmholtz Munich | Open to postdoctoral & staff scientist positions
I am a computational biologist with a PhD from Helmholtz Munich, where I developed methods for multi-omics data integration and gene regulatory network (GRN) inference applied to chronic lung diseases — including bronchopulmonary dysplasia (BPD), asthma, and COPD.
My work spans bulk and single-cell transcriptomics, proteomics, metabolomics, CITE-seq, and scTCR-seq. I contribute to open-source tools in the R/Bioconductor and Python ecosystems, and I care deeply about reproducible, well-documented scientific software.
Languages
Frameworks & Libraries
Infrastructure
| Project | Description | Stack |
|---|---|---|
| omicslog | Logging-aware for SummarizedExperiment and AnnData objects for auditable omics workflows |
R · Python · Bioconductor · scverse |
| KiMONo | Knowledge-guided multi-omics network inference framework | R · Multi-omics · GRN |
| cellNexusPy | Unified single-cell data access layer with Python API contributions | Python · scRNA-seq |
| Biocfomo | R wrapper for spatial transcriptomics foundation models via basilisk |
R · GPU · Spatial |
Henao J et al. (2023). Multi-omics regulatory network inference in the presence of missing data. Briefings in Bioinformatics, 24(5), bbad309. → DOI
Henao J et al. (2025). Multi-omic signatures relate to the severity of pulmonary outcome in neonates traced into adult disease. Network and Systems Medicine. → DOI
Last updated May 2026