Causal Representation Learning – A Proposal

Abstract: The development of CRISPR-based assays and small molecule screens holds the promise of engineering precise cell state transitions to move cells from one cell type to another or from a diseased state to a healthy state. The main bottleneck is the huge space of possible perturbations/interventions, where even with the breathtaking technological advances in single-cell biology it will never be possible to experimentally perturb all combinations of thousands of genes or compounds. This important biological problem calls for a framework that can integrate data from different modalities to identify causal representations, predict the effect of unseen interventions, and identify the optimal interventions to induce precise cell state transition. Traditional representation learning methods, although often highly successful in predictive tasks, do not generally elucidate causal relationships. In this talk, we will present initial ideas towards building a statistical and computational framework for causal representation learning and its application towards optimal intervention design.

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Bio: Caroline Uhler is the Henry L. and Grace Doherty associate professor in EECS and IDSS, a member of SDSC, LIDS and the ORC, Machine Learning at MIT, and is also core member of the Broad Institute, where she co-directs the Eric and Wendy Schmidt Center.  She is an elected member of the International Statistical Institute and the recipient of a Simons Investigator Award, a Sloan Research Fellowship, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Fund.