Christopher Heje Grønbech

• Presentation
• Research outputs

I am PhD student employed by the bioinformatics software company Qlucore in Lund, Sweden, enrolled at University of Copenhagen, and funded by the Marie Skłodowska-Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine”.

I develop deep-learning methods to model and visualise biomedical data, in particular single-cell RNA-sequencing gene expression data, to improve the interpretability of the data.

These methods are deep generative methods, in particular variational auto-encoders (VAEs), and I have worked with Gaussian-mixture VAEs, conditional VAEs, hierarchical VAEs, and mixtures of these.

Selected publications

1. Published

   scVAE: variational auto-encoders for single-cell gene expression data

   Grønbech, Christopher Heje, Vording, M. F., Timshel, P., Sønderby, C. K., Pers, Tune H & Winther, Ole, 2020, In: Bioinformatics. 36, 16, p. 4415-4422 8 p.

   Research output: Contribution to journal › Journal article › peer-review