Nicholas H. Nelsen

Biography

Nicholas Nelsen is an Assistant Professor at UT Austin, where he holds a joint appointment in the Oden Institute (Principal and GSC Faculty) and the Department of ASE/EM. Before joining UT in August 2026, he was a Klarman Fellow in the Department of Mathematics at Cornell University (2025-2026) and an NSF Postdoc in the Department of Mathematics at MIT (2024-2025). Nelsen earned his Ph.D. from Caltech in 2024, where his doctoral dissertation on data-efficient operator learning was awarded the W. P. Carey & Co. Prize for Best Thesis in Applied Mathematics and the Centennial Prize for the Best Thesis in MCE.

Nelsen’s research centers on computational mathematics and machine learning. He develops data-driven methods for high- or infinite-dimensional problems and establishes mathematical guarantees on the reliability of these methods. His fundamental work is motivated by applications in the physical, engineering, and data sciences. Some of Nelsen’s specific research interests include operator learning for parametrized partial differential equations, statistical and stochastic inverse problems, non-Euclidean data analysis for generative modeling of probability distributions, approximate Bayesian inference and uncertainty quantification, optimal sampling and experimental design, and data assimilation for dynamical systems.

Google Scholar