Nicholas Nelsen

Nicholas Nelsen is a Klarman Fellow in the Department of Mathematics at Cornell University. His research focuses on developing statistical and machine learning methods for high- or infinite-dimensional problems within applied and computational mathematics. His work integrates operator learning with concepts from inverse problems, generative modeling, and uncertainty quantification, with a current emphasis on probability measures. Nelsen's research includes topics such as operator learning, inverse problems, and scientific computing. He has published in journals such as SIAM Review, Foundations of Data Science, and Advances in Neural Information Processing Systems.

Nicholas H. Nelsen is a researcher in applied mathematics, machine learning, inverse problems, uncertainty quantification, and statistics. His work focuses on developing mathematical frameworks for learning operators from data, with applications in scientific computing and inverse problems. He has published on topics such as random feature models, operator learning, and probabilistic perspectives on inverse problems. His research also includes statistical foundations of operator learning and the development of tools for scientific machine learning. Nelsen's contributions span both theoretical and applied aspects of learning from noisy data and improving the efficiency of scientific simulations.