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 artificial intelligence (AI) methods for high- or infinite-dimensional problems and establishes mathematical guarantees on the reliability of these methods. His fundamental work is instated in physical, engineering, and data science applications; these include weather forecasting, fusion energy, and intelligent systems. Some of Nelsen’s specific research interests include operator learning beyond parametrized partial differential equations, amortized Bayesian inference and uncertainty quantification, statistical and stochastic inverse problems, optimal sampling and experimental design, data assimilation for dynamical systems, generative AI, and non-Euclidean data analysis.