Nicholas H.Nelsen
Uncertainty quantification; Statistics; Applied probability; Inverse problems; Scientific machine learning
About
Nicholas Nelsen will join us as an assistant professor at The University of Texas at Austin in Fall 2026, where he will hold a joint appointment in the Department of Aerospace Engineering and Engineering Mechanics and the Oden Institute for Computational Engineering and Sciences. He was a Klarman Fellow in the Department of Mathematics at Cornell University (2025-2026) and held a National Science Foundation postdoctoral position in the Department of Mathematics at MIT (2024-2025). Nelsen earned his Ph.D. from Caltech in 2024, where his doctoral dissertation on the data efficiency of scientific machine 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 the interplay between machine learning and computational science, engineering, and mathematics. He develops data-driven methods for high- or infinite-dimensional problems and establishes mathematical guarantees on the reliability of these methods. This fundamental work is realized in applications in the physical, engineering, and data sciences, such as weather forecasting, medical imaging and materials modeling. Some of Nelsen’s specific research interests include operator learning for parametrized partial differential equations, statistical and stochastic inverse problems, generative modeling and artificial intelligence, approximate Bayesian inference and uncertainty quantification, optimal sampling and experimental design, and data assimilation for dynamical systems.
Educational Qualifications
Ph.D., California Institute of Technology
B.S., Aerospace Engineering, Oklahoma State University
B.S., Mechanical Engineering, Oklahoma State University
B.Sc., Mathematics, Oklahoma State University