Nicholas F. Marshall
Assistant Professor
Department of Mathematics
Oregon State University
Office: Kidder Hall 292
Email: [email protected]
Research interests
I am interested in problems that involve an interplay between analysis, geometry,
and probability (especially such problems motivated by data
science).
About
Research
My arXiv author identifier is marshall_n_1

arXiv:2212.14288
From the binomial reshuffling model to Poisson distribution of money
with
Fei Cao

arXiv:2210.17501
Fast Principal Component Analysis for CryoEM Images
with
Oscar Mickelin,
Yunpeng Shi
and
Amit Singer

arXiv:2207.13674
Fast expansion into harmonics on the disk: a steerable basis with fast radial convolutions
with
Oscar Mickelin
and
Amit Singer

arXiv:2202.12224
An optimal scheduled learning rate for a randomized Kaczmarz algorithm
with
Oscar Mickelin
SIAM Journal on Matrix Analysis and Applications (to appear)

arXiv:2201.13386
On a linearization of quadratic Wasserstein distance
with
Philip Greengard
,
Jeremy Hoskins
and Amit Singer

arXiv:2107.14747
A common variable minimax theorem for graphs
with
Ronald Coifman
and
Stefan Steinerberger
Foundations of Computational Mathematics
doi.org/10.1007/s10208022095588

arXiv:2101.07709
Multitarget detection with rotations
with
Tamir Bendory,
TiYen Lan,
Iris Rukshin,
and Amit Singer
Inverse Problems and Imaging doi.org/10.3934/ipi.2022046

arXiv:1910.10006
Image recovery from rotational and translational invariants
with
Tamir Bendory,
TiYen Lan,
and Amit Singer
ICASSP
doi.org/10.1109/ICASSP40776.2020.9053932

arXiv:1910.04201
Randomized mixed Hölder function approximation in higherdimensions
Technical Report

arXiv:1907.03873
A fast simple algorithm for computing the potential of charges on a line
with
Zydrunas Gimbutas and Vladimir Rokhlin
Applied and Computational Harmonic Analysis
doi.org/10.1016/j.acha.2020.06.002

arXiv:1902.06633
A Cheeger inequality for graphs based on a reflection
principle
with
Edward Gelernt, Diana Halikias, and Charles Kenney
Involve
doi.org/10.2140/involve.2020.13.475

arXiv:1810.00823
Approximating mixed Hölder functions using random samples
Annals of Applied Probability
doi.org/10.1214/19AAP1471

arXiv:1711.06711
Manifold learning with bistochastic kernels
with
Ronald Coifman
IMA Journal of Applied Mathematics
doi.org/10.1093/imamat/hxy065

arXiv:1707.00682
Stretching convex domains to capture many lattice points
International Mathematics Research Notices
doi.org/10.1093/imrn/rny102

arXiv:1706.04170
Triangles capturing many lattice points
with
Stefan Steinerberger
Mathematika
doi.org/10.1112/S0025579318000219

arXiv:1704.02962
The Stability of the First Neumann Laplacian Eigenfunction
Under Domain Deformations and Applications
Applied and Computational Harmonic Analysis
doi.org/10.1016/j.acha.2019.05.001

arXiv:1608.03628
Time Coupled Diffusion Maps
with
Matthew Hirn
Applied and Computational Harmonic Analysis
doi.org/10.1016/j.acha.2017.11.003

arXiv:1607.05235
Extracting Geography from Trade Data
with
Yuke Li, Tianhao Wu,
and Stefan
Steinerberger
Physica A
doi.org/10.1016/j.physa.2017.01.037
Notes
Some short notes on various topics

somemathfornumerics.pdf
Introductory note about some key mathematical ideas for used in
numerical methods including:
asymptotic series, Richardson extrapolation, contraction mapping,
and simple
iteration.

stirlingsapproximation.pdf
Elementary proof of Stirling's approximation up to constant using:
concave functions, trapezoid rule, midpoint
rule

eulermaclaurin.pdf
Informal and precise statements of EulerMaclaurin formula with
preliminaries about:
asymptotic series, Richardson extrapolation, Taylor's theorem, Trapezoid rule

gaussianquadrature.pdf
Introduction to Gaussian quadrature including discussion of:
Legendre polynomials, Gaussian quadrature remainder formula, numerical example

chebyshevinterpolation.pdf
Introduction to polynomial interpolation focusing on:
polynomial interpolation remainder formula, Chebyshev polynomials, Chebyshev
nodes

demoivrethm.pdf
Sketch of de Moivre's central limit theorem involving:
Binomial distribution, Stirling's formula, Reimann sum
Mentoring
Teaching