AI & Fundamentals
Causality and Autoencoders in the Light of Drug Repurposing for COVID-19 - Caroline Uhler, Associate Professor, MIT

Caroline Uhler image

DATE: Tue, March 1, 2022 - 2:00 pm

LOCATION: Please register to receive the Zoom link


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Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (for example in genomics, advertisement, education, etc.). In order to obtain mechanistic insights from such data, a major challenge is the development of a framework that integrates observational and interventional data and allows causal transportability, i.e., predicting the effect of yet unseen interventions or transporting the effect of interventions observed in one context to another. I will propose an autoencoder framework for this problem. In particular, I will characterize the implicit bias of overparameterized autoencoders and show how this links to causal transportability and can be applied for drug repurposing in the current COVID-19 crisis.


Caroline Uhler co-directs the newly-launched Eric and Wendy Schmidt Center at the Broad Institute and is an associate professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT. She holds an MSc in mathematics, a BSc in biology, and an MEd all from the University of Zurich. She obtained her PhD in statistics from UC Berkeley in 2011 and then spent three years as an assistant professor at IST Austria before joining MIT in 2015. She is a Simons Investigator, a Sloan Research Fellow, and an elected member of the International Statistical Institute. In addition, she received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Her research lies at the intersection of machine learning, statistics, and genomics, with a particular focus on causal inference and gene regulation.


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