Aditya’s interests are mainly in probabilistic machine learning and he works on probabilistic interpretations of dimensionality reduction methods used in single cell workflows. His current work focuses on interpretations of methods such as (t-)SNE and UMAP, which can be thought of as latent graph inference methods with connections to graph Gaussian processes, and DTW-type algorithms which have connections to HMMs and are widely applicable.

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