Björn Sandstede

The paper introduces ctOTVelo, a method for inferring cell type-specific gene regulatory networks from single-cell transcriptomics data, achieving state-of-the-art performance.

SCOT+ aligns single-cell multi-omics data across datasets using optimal transport without requiring correspondence, improving downstream analysis accuracy.

OTVelo estimates gene velocities using optimal transport to infer dynamic gene regulatory networks from time-stamped single-cell gene expression data.

SCOT uses Gromov-Wasserstein optimal transport to align single-cell multi-omics data without requiring correspondence information for hyperparameter tuning, performing on par with state-of-the-art methods but faster.

The paper improves SCOT for integrating single-cell multi-omics datasets with imbalanced cell-type representation and varying sample sizes, showing better alignment performance on real-world datasets.

The paper introduces WINGS, a method using hierarchical clustering to identify shared genetic architecture among multiple traits in the UK Biobank, finding 10 significant phenotype clusters.

This paper introduces TDCCA, a method for analyzing multilevel time series data, and shows it detects temporal changes and improves feature extraction in fMRI studies.