Nicholas F. Marshall
Department of Mathematics
Princeton, NJ 08544
Office: Fine 213
- I am an NSF Postdoc at Princeton sponsored by Amit Singer and
supported by NSF
- I am also part of the Simons Collaboration on Algorithms
- My research interest is in harmonic analysis and its
- In particular, I am interested in problems that involve interplay
between analysis, geometry, and probability.
- I completed my PhD in applied math at Yale in May 2019 under the
You can find me on the mathematics genealogy project
- During the summer of 2018 I was a mentor for a SUMRY undergraduate research group
(see our paper arXiv:1902.06633 below).
- Here's a photo of me while studying Math in Moscow.
Image recovery from rotational and translational invariants
and Amit Singer
Randomized mixed Hölder function approximation in higher-dimensions
A fast simple algorithm for computing the potential of charges on a line
Zydrunas Gimbutas and Vladimir Rokhlin
Applied and Computational Harmonic Analysis 49 no. 3
A Cheeger inequality for graphs based on a reflection
Edward Gelernt, Diana Halikias, and Charles Kenney
Involve 13 no. 3, (2020)475--486.
Approximating mixed Hölder functions using random samples
Annals of Applied Probability 29 no. 5 (2019):2988--3005.
Manifold learning with bi-stochastic kernels
IMA Journal of Applied Mathematics
84 no. 3 (2019): 455--482.
Stretching convex domains to capture many lattice points
International Mathematics Research Notices 2020 no. 10
Triangles capturing many lattice points
Mathematika, 64 no. 2 (2018):551--582.
The Stability of the First Neumann Laplacian Eigenfunction
Under Domain Deformations and Applications
Applied and Computational Harmonic Analysis (to appear), 2019.
Time Coupled Diffusion Maps
Applied and Computational Harmonic Analysis,
45 no. 3 (2018):709--728.
Extracting Geography from Trade Data
Yuke Li, Tianhao Wu,
Physica A, 473 (2017):205--212.