An exciting opportunity at the intersection of the biomedical sciences and machine learning stems from the growing availability of large-scale multi-modal data (imaging-based and sequencing-based, observational and perturbational, at the single-cell level, tissue-level, and organism-level). Traditional representation learning methods, although often highly successful in predictive tasks, do not generally elucidate underlying causal mechanisms. I will present a statistical and computational framework for causal representation learning and its applications towards identifying novel disease biomarkers as well as inferring gene regulation in different disease contexts.
Caroline Uhler is a core institute member of the Broad Institute of MIT and Harvard, where she directs the Eric and Wendy Schmidt Center and is a member of the Scientific Leadership Team. She is also the Andrew (1956) and Erna Viterbi Professor of Engineering in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT.
Caroline’s research lies at the intersection of machine learning, statistics, and genomics, with a particular focus on causal inference, representation learning, and gene regulation.
Caroline is recognized as a creative and innovative researcher and teacher at the intersection of machine learning, statistics, and biology. She is a SIAM Fellow, a Fellow of the IMS, a Sloan Research Fellow, and an elected member of the International Statistical Institute. In addition, she has received multiple awards including an NIH New Innovator Award, a Simons Investigator Award, and an NSF Career Award.
Caroline 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 the faculty at MIT in 2015.
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