Caroline Uhler
Andrew (1956) and Erna Viterbi Professor of Engineering, Department of Electrical Engineering and Computer Science; Director, Eric and Wendy Schmidt Center at Broad Institute
Who they work with
Caroline Uhler is the Andrew (1956) and Erna Viterbi Professor of Engineering and a professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS). She directs the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, where she’s a core institute member and is a member of the scientific leadership team. Her research focuses on machine learning and statistics, with applications to single-cell biology, in particular on graphical models and causal inference, neural networks for representation learning and generative modeling, and their applications to studying gene regulatory networks based on a variety of single-cell data modalities.
Uhler 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. She earned a BS in biology, an MS in mathematics, and an MEd from the University of Zurich, and a PhD in statistics from the University of California, Berkeley.
Selected Publications
- Paysan, D., Radhakrishnan, A., Zhang, X., Shivashankar, G. V., & Uhler, C. (2025). Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens. Cell Systems, 16(6), 101293.
- Mazaheri, B., Squires, C., & Uhler, C. (2025). Synthetic potential outcomes and causal mixture identifiability. In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS) (Proceedings of Machine Learning Research, Vol. 258, pp. 4276–4284). PMLR.
- Zhang, X., Venkatachalapathy, S., Paysan, D., Schaerer, P., Tripodo, C., Uhler, C., & Shivashankar, G. V. (2024). Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS. Nature Communications, 15, 6112.
Media
- September 2, 2025: MIT News, 3 Questions: On biology and medicine’s “data revolution”
- September 10, 2024: MIT News, School of Engineering faculty and staff receive awards in spring 2024
- July 22, 2024: MIT News, AI model identifies certain breast tumor stages likely to progress to invasive cancer