Projects

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

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.

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?

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

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