Caroline Uhler (born 1983) is a Swiss statistician working in the field of Machine learning and applications in genomics. Her research focuses on developing methods for causal inference to infer regulatory relationships from different data modalities, including transcriptomic, proteomic, and structural data. She is a Full Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society at the Massachusetts Institute of Technology. In addition, she is a Core Institute Member at the Broad Institute, where she directs the Eric and Wendy Schmidt Center.
Education and career
Uhler was born in Switzerland. She studied mathematics and biology at the University of Zurich, earning a bachelor's degree in mathematics in 2004, a second bachelor's degree in biology and a master's degree in mathematics in 2006. She stayed at the university for a credential as a high school mathematics teacher in 2007, but instead of becoming a teacher she traveled to the US for graduate education at the University of California, Berkeley. There, she earned both a Ph.D. in statistics and a degree in management of technology from the Haas School of Business in 2011. Her doctoral dissertation, "Geometry of maximum likelihood estimation", "Gaussian graphical models", was supervised by Bernd Sturmfels, an algebraic geometer and algebraic statistician.
Uhler became an assistant professor at the Institute of Science and Technology Austria in 2012, after a short postdoc at the Institute for Mathematics and its Applications at the University of Minnesota as well as at ETH Zurich. She moved to the Massachusetts Institute of Technology in 2015 as the Henry L. and Grace Doherty Assistant Professor, was promoted to associate professor in 2018 and to full professor in 2022. Since 2022, she has also been a Core Institute Member at the Broad Institute, where she directs the Eric and Wendy Schmidt Center.
Research contributions
Uhler's work sits at the intersection of artificial intelligence, statistics, and biology. She develops mathematical frameworks for causal inference that help decipher regulatory networks from high-dimensional biological measurements, such as gene expression data from single cells or proteomic profiles. Her group has contributed algorithms that combine ideas from graphical models and convex optimization to reconstruct gene regulatory interactions, often integrating multiple data modalities to improve robustness.
One strand of her research involves the geometry of Gaussian graphical models, extending conceptual results from algebraic statistics to practical tools for inference. Another significant area is the development of interpretable machine learning methods that can handle large-scale genomics data, aiming to bridge gaps between statistical guarantees and real-world noisy observations. These efforts have shaped her students' work on data augmentation techniques and robust procedures.
Recognition
Uhler has received multiple career honors. In 2014, she became an elected member of the International Statistical Institute. In 2015, she won the Start-Preis of the Austrian Science Fund and the Sofia Kovalevskaja Award of the Alexander von Humboldt Foundation, declining the latter funding to move to MIT. In 2017, she received the NSF CAREER Award and the Sloan Research Fellowship. In 2019, she was named a Simons Investigator in Mathematical Modeling of Living Systems, recognizing her pathways in biomathematics. In 2022, she earned the NIH Director's New Innovator Award. In 2023, she was elected as a Fellow of the Society for Industrial and Applied Mathematics (SIAM).
Selected publications and impact
Across her career, Uhler has published in venues spanning journals from statistics, machine learning, and computational biology. Her theses and work attracted citations not only from statistics but also from practitioners using deep learning models for genomics. The algorithms developed by her lab are often used by researchers investigating disease mechanisms, including those in precision medicine. She has also contributed to the field's educational pipeline by mentoring young researchers who have gone on to academic positions or science industries.
External links
- Home page
- Caroline HI publications indexed by Google HScholar