Nicholas Nelsen

Bio

Nicholas Nelsen is an NSF Mathematical Sciences Postdoctoral Research Fellow working at the intersection of applied mathematics and statistics. He develops novel machine learning methods for high- and infinite-dimensional problems, establishes theoretical guarantees on the reliability and trustworthiness of these methods, and applies them in the physical and data sciences. His current research involves blending operator learning with ideas from inverse problems, generative modeling, and uncertainty quantification.

Nicholas received his Ph.D. from Caltech under the supervision of Andrew Stuart. His dissertation was awarded the W.P. Carey and Co. Prize in Applied Mathematics and the Centennial Prize for the Best Thesis in MCE. Nicholas also won a SIAM Review SIGEST award for his work on random features.