Caroline Uhler

Caroline Uhler is a professor in MIT’s Department of Electrical Engineering and Computer Science and an investigator in the Institute for Data, Systems, and Society. She is also co-director of The Eric and Wendy Schmidt Centre at the Broad Institute. 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 Simons Investigator, a Sloan Research Fellow, and an elected member of the International Statistical Institute. She has received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. 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

• Zhang, J., Greenewald, K., Squires, C., Srivastava, A., Shanmugam K., Uhler, C. (2023). Identifiability Guarantees for Causal Disentanglement from Soft Interventions. Conference on Neural Information Processing Systems (NeurIPS).
• Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A. A., Uhler, C. (2023). Cross-modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State. Nature Communications.
• Radhakrishnan, A., Belkin, M., Uhler, C. (2023). Wide and Deep Neural Networks Achieve Consistency for Classification. Proceedings of the National Academy of Sciences (PNAS).
• Radhakrishnan, A., Stefanakis, G., Belkin, M., Uhler, C. (2022). Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks. Proceedings of the National Academy of Sciences (PNAS).
• Agrawal, R., Shanmugam, K., Squires, C., Uhler, C., Yang, K. D. (2019). ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. Proceedings of Machine Learning Research 89 (AISTATS), pp. 3400-3409.
• Katz, D., Shanmugam, K., Squires, C., Uhler, C. (2019). Size of Interventional Markov Equivalence Classes in Random DAG Models  Proceedings of Machine Learning Research 89 (AISTATS), pp. 3234-3243.

Media

• February 16, 2021: MIT News, A machine-learning approach to finding treatment options for Covid-19.
• January 31, 2020: LIDS News, LIDS faculty member Caroline Uhler and LIDS Affiliate Member Elchanan Mossel receive Simons Investigator Awards.
• May 25,2018: IDSS News, Caroline Uhler awarded J-WAFS Seed Grant for research on climate impacts on agriculture.
• April 12, 2017: LIDS News, Caroline Uhler receives 2017 NSF Career Award.
• February 21, 2017: MIT News, Women in Data Science conference highlights impactful work and builds community.