Caroline Uhler

Henry L. Grace Doherty Assistant Professor 

Department of Electrical Engineering and Computer Sceience and Institure of Data, Systems and Society


  • Institute for Data, Systems and Society (IDSS)
  • Electrical Engineering and Computer Science (EECS)


32-D634 & E17-467
(617) 253-4181

Faculty Bio: 

Caroline Uhler joined the MIT faculty in 2015 as the Henry L. and Grace Doherty assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. She holds an MSc in mathematics, a BSc in biology, and an MEd in high school mathematics education from the University of Zurich. She obtained her PhD in statistics, with a designated emphasis in computational and genomic biology, from the University of California, Berkeley. Before joining MIT, she spent a semester as a research fellow in the program on “Theoretical Foundations of Big Data Analysis” at the Simons Institute at UC Berkeley, postdoctoral positions at the Institute of Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant  professor at IST Austria. She is an elected member of the International Statistical Institute, she is a Sloan Research Fellow, and 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 focuses on mathematical statistics and computational biology, in particular on graphical models and causal inference to learn gene regulatory networks and the development of geometric models for the organization of chromosomes.

Research Areas: 

Research Summary: 

My group studies probabilistic graphical models and develops theory, methodology and algorithms to allow application of these models to scientifically important novel problems. In particular, our work to date has broken new grounds on providing a systematic approach to studying graphical models. We use a holistic approach that combines ideas from applied algebraic geometry, combinatorics, convex optimization, mathematical statistics, and machine learning. By leveraging the inherent algebraic structure in graphical models, we have uncovered statistical and computational limitations for learning directed graphical models to perform causal inference. In addition, my group develops scalable algorithms with provable guarantees for learning graphical models in genomics, in particular for learning gene regulatory networks. Gene regulation is inherently linked to the spatial organization of the DNA in the cell nucleus. In order to understand the mechanisms underlying gene regulation, my group works towards deciphering the codes that link the packing of the DNA with gene expression. Towards this goal, my group has introduced a new geometric model for the organization of chromosomes that is based on the theory of packing in mathematics: we view a chromosome configuration  as a minimal overlap configuration of ellipsoids. My group has successfully applied this model to predict the reorganization of chromosomes that happen during changes of cell shape as they occur for example during reprogramming. We envision that such models will provide important insights into processes such as differentiation, trans-differentiation or reprogramming, where it is essential to understand the coupling between cell shape, chromosome organization and gene regulation.