Nicholas H. Nelsen

NSF Graduate Research Fellow and Ph.D. Candidate

California Institute of Technology

About Me

Welcome! I am a fourth year graduate student in the Division of Engineering and Applied Science at Caltech, where I work with my advisor Prof. Andrew M. Stuart, and my research interests are in theory and algorithms for high-dimensional scientific and data-driven computation.

My current work is centered on operator regression—learning, from (noisy) data, operators that map between infinite-dimensional (function) spaces—with application to forward and inverse problems arising from parametric partial differential equations (PDEs) that model physical systems. To this end, I develop and utilize tools from machine learning, model reduction, and numerical/statistical analysis. To learn more about my background and research experience, please refer to my curriculum vitae and my publications page.

I am fortunate to be supported by a NSF Graduate Research Fellowship. In 2020, I obtained my M.Sc. from Caltech, and before starting doctoral study in the fall of 2018, I worked on Lagrangian particle methods for PDEs as a summer research intern in the Center for Computing Research at Sandia National Laboratories. I obtained my B.Sc. (Mathematics), B.S.M.E., and B.S.A.E. degrees from Oklahoma State University in 2018.

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