Computational Morphogenomics Group Dumitrascu Lab @CL, University of Cambridge

Projects

You can learn more about ongoing specific projects below.

Explainability + Genomics

Dimensionality Reduction Algorithms with a focus on explainability for single cell transcriptomics experiments.

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Physics + ML

Physics informed machine learning tools for understanding self-organizing systems. Multi-agent simulations, graph neural networks and symbolic regression models for in silico active matter, developmental patterns and ecology.

In vitro vs in vivo systems

Transfer learning and multi-modal learning for organoid to scRNA-seq integration and comparison. What gene regulatory signatures encountered in vivo are recapitulated in organoid cultures?

Mechanics + Transcriptomics

Bayesian machine learning methods for inferring mechanical properties and morphological features of cellular aggregates with the goal of quantifying their interaction with transcriptomics.

Infer + Perturb

Experimental design and multi arm bandits for optimal perturbations in transcriptomic studies.