Caroline Moosmueller

Caroline Moosmueller is an Assistant Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill, where she leads the Geometric Data Analysis research group. She holds a B.Sc and M.Sc in Mathematics from the University of Vienna and a Ph.D in Technical Mathematics from Graz University of Technology. Her professional background includes postdoctoral work at Johns Hopkins University (2017-2019) and a Visiting Assistant Professor position at the University of California, San Diego (2019-2022).

Her research develops numerical methods for nonlinear and high-dimensional data analysis with focus on structure-preserving algorithms. Key areas include:

• Computational optimal transport and Wasserstein space analytics
• Geometric machine learning and classification tasks
• Approximation theory for biological and medical applications
• Dimensionality reduction techniques for complex datasets

Publication analysis reveals strong focus on optimal transport theory applications in machine learning and biomedicine, with recent work exploring trajectory inference, dimensionality reduction, and stochastic measure transport methods. Her papers consistently integrate theoretical rigor with computational implementations.

Awards include:

• J. Burton Linker Fellowship

She leads multiple funded projects:

• NSF awards DMS 2111322, 2306064, 2410140
• UNC School of Data Science Seed Grant for "Spatio-temporal analysis of brain functional connectome" (with Kovalsky/Styner/Wu)

and mentors seven graduate students. Her lab focuses on geometric algorithms for cancer research and biomedical data analysis.