Caroline Uhler, Ph.D.

Department of Electrical Engineering and Computer Science
MIT Laboratory for Information and Decision Systems

Room 32-D634
617-253-4181 (phone)


Caroline Uhler joined the MIT faculty in October 2015 as an 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, and she received a Sofja Kovalevskaja Award from the Humboldt Foundation and a START Award from the Austrian Science Foundation. More recently, she was awarded a Sloan Research Fellowship and an NSF Career Award. 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 Summary

My group studies probabilistic graphical models and develops theory, methodology and algorithms to allow application of these models to scientifically important novel applications. In particular, our work to date has broken new grounds on providing a systematic approach to studying Gaussian 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 happens 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.

Selected Publications

  • Uhler, C., Lenkoski, A. and Richards, D., "Exact formulas for the normalizing constants of Wishart distributions for graphical models", to appear in Annals of Statistics, preprint available at
  • Zwiernik, P., Uhler, C. and Richards, D., "Maximum likelihood estimation for linear Gaussian covariance models", to appear in Journal of the Royal Statistical Society Series B, preprint available at
  • Uhler, C. and Shivashankar, G.V., "Chromosome intermingling: Mechanical hotspots for genome regulation", Trends in Cell Biology (2017), invited review.
  • Uhler, C. and Wright, S.J., "Packing ellipsoids with overlap", SIAM Review 55 (2013), pp. 671-706 (selected as Research Spotlight).
  • Uhler, C., Raskutti, G., Bühlmann, P. and Yu, B., "Geometry of faithfulness assumption in causal inference", Annals of Statistics 41 (2013), pp. 436-463.
  • Uhler, C., Fienberg, S.E. and Slavkovic, A., Privacy-preserving data sharing for genome-wide association studies", Journal of Privacy and Confidentiality 5 (2013), pp. 137-166.
  • Uhler, C., "Geometry of maximum likelihood estimation in Gaussian graphical models", Annals of Statistics 40 (2012), pp. 238-261.


  • Sloan Research Fellowship (2017)
  • NSF Career Award (2017)
  • Charles E. Reed Faculty Initiative Fund Award (2016)
  • Doherty Professorship in Ocean Utilization (2015)
  • START Award from the Austrian Science Fund (2015)
  • Sofja Kovalevskaja Award (2015)
  • Elected Member of the International Statistical Institute (ISI) (2014)


Last Updated: March 19, 2017