Nicholas F. Marshall

  
  Assistant Professor
  Department of Mathematics
  Oregon State University
  
  Email: [email protected]
  Office: Kidder 292
  Directory
  Link
  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
  
    - 
      arXiv:2510.24608
 Random Walks, Faber Polynomials and Accelerated Power Methods
 with Peter Cowal, Sara Pollock
 
- 
      arXiv:2507.01885
 Faber polynomials in a deltoid region and power iteration momentum methods
 with Peter Cowal, Sara Pollock
 
- 
      arXiv:2406.05922
 Fast expansion into harmonics on the ball
 with Joe
      Kileel, Oscar Mickelin,
      Amit
      Singer
 SIAM Journal on Scientific Computing doi.org/10.1137/24M1668159
 
- 
      arXiv:2406.01552
 Learning equivariant tensor functions with applications to
      sparse vector recovery
 with Wilson G.
      Gregory, Josué
      Tonelli-Cueto, Andrew S. Lee,
      Soledad
      Villar
 
- 
      arXiv:2404.10759
 Laplace-HDC: Understanding the geometry of binary
      hyperdimensional computing
 with 
      Saeid Pourmand, Wyatt
      Whiting, Alireza Aghasi
 Journal of Artificial Intelligence Research doi.org/10.1613/jair.1.17688
 
- 
      arXiv:2401.15183
 Moment-based metrics for molecules computable from cryo-EM
      images
 with Andy
      Zhang, Oscar Mickelin,
      Joe
      Kileel, Eric
      Verbeke, Marc
      Gilles, Amit Singer
 Biological Imaging doi.org/10.1017/S2633903X24000023
 
- 
      arXiv:2401.09415
 Randomized Kaczmarz with geometrically smoothed
      momentum
 with 
      Seth Alderman, Roan Luikart
 SIAM Journal on Matrix Analysis and Applications
      doi.org/10.1137/24M1633820
 
- 
      arXiv:2212.14288
 From the binomial reshuffling model to Poisson
      distribution of money
 with Fei Cao
 Networks and Heterogeneous Media doi.org/10.3934/nhm.2024002
 
- 
      arXiv:2210.17501
 Fast Principal Component Analysis for Cryo-EM
      Images
 with Oscar
      Mickelin, Yunpeng Shi, Amit Singer
 Biological Imaging doi.org/10.1017/S2633903X23000028
 
- 
      arXiv:2207.13674
 Fast expansion into harmonics on the disk: a steerable
      basis with fast radial convolutions
 with Oscar
      Mickelin, Amit Singer
 SIAM Journal on Scientific Computing doi.org/10.1137/22M1542775
 
- 
      arXiv:2202.12224
 An optimal scheduled learning rate for a randomized
      Kaczmarz algorithm
 with Oscar
      Mickelin
 SIAM Journal on Matrix Analysis and Applications
      doi.org/10.1137/22M148803X
 
- 
      arXiv:2201.13386
 On a linearization of quadratic Wasserstein
      distance
 with 
      Philip Greengard, Jeremy Hoskins, Amit Singer
 
- 
      arXiv:2107.14747
 A common variable minimax theorem for graphs
 with 
      Ronald Coifman, Stefan
      Steinerberger
 Foundations of Computational Mathematics doi.org/10.1007/s10208-022-09558-8
 
- 
      arXiv:2101.07709
 Multi-target detection with rotations
 with Tamir
      Bendory, 
      Ti-Yen Lan, 
      Iris Rukshin, 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, 
      Ti-Yen Lan, Amit Singer
 ICASSP doi.org/10.1109/ICASSP40776.2020.9053932
 
- 
      arXiv:1910.04201
 Randomized mixed Hölder function approximation in
      higher-dimensions
 Technical Report
 
- 
      arXiv:1907.03873
 A fast simple algorithm for computing the potential of
      charges on a line
 with Zydrunas
      Gimbutas, 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,
      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/10.1214/19-AAP1471
 
- 
      arXiv:1711.06711
 Manifold learning with bi-stochastic 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, Stefan
      Steinerberger
 Physica A doi.org/10.1016/j.physa.2017.01.037
Notes
  Some short notes on various topics
  
    - 
      some-math-for-numerics.pdf
      
        - Introductory note about some key mathematical ideas
        used in numerical methods
- Discusses asymptotic series, Richardson extrapolation,
        contraction mapping, and simple iteration
 
- 
      stirlings-approximation.pdf
      
        - Elementary proof of Stirling's approximation up to
        constant
- Discusses concave functions, trapezoid rule, midpoint
        rule
 
- 
      euler-maclaurin.pdf
      
        - Informal and precise statements of Euler-Maclaurin
        formula
- Preliminaries about asymptotic series, Richardson
        extrapolation, Taylor's theorem, Trapezoid rule
 
- 
      gaussian-quadrature.pdf
      
        - Introduction to Gaussian quadrature
- Introduces Legendre polynomials, Gaussian quadrature
        remainder formula, numerical example
 
- 
      chebyshev-interpolation.pdf
      
        - Introduction to polynomial interpolation
- Includes polynomial interpolation remainder formula,
        Chebyshev polynomials, Chebyshev nodes
 
- 
      de-moivre-thm.pdf
      
        - Sketch of de Moivre's central limit theorem
- Uses Binomial distribution, Stirling's formula, Reimann
        sum
 
Mentoring
  
    - Graduate Students
      
    
- Undergraduate Students
      
        - Summer research 2023 at Oregon State
 
- Summer research 2020 at Princeton
 
- Summer research 2018 at Yale
          
        
 
Teaching
  
    - 
      Fall 2025
      
        - Advanced Calculus I, MTH 611, Oregon State University
- Numerical Linear Algebra, MTH 451, Oregon State University
- Probability I, MTH 463, Oregon State University
 
- 
      Spring 2025
      
        - Complex Analysis, MTH 611, Oregon State University
 
- 
      Winter 2025
      
        - Advanced Calculus II, MTH 312, Oregon State
        University
- Mathematics of Data Science, MTH 499/599, Oregon State
        University
 
- 
      Fall 2024
      
        - Probability Theory I, MTH 664, Oregon State
        University
 
- 
      Spring 2024
      
        - High Dimensional Probability, MTH 669, Oregon State
        University
 
- 
      Winter 2024
      
        - Probability Theory II, MTH 665, Oregon State
        University
 
- 
      Fall 2023
      
        - Probability Theory, MTH 664, Oregon State
        University
 
- 
      Spring 2023
      
        - Advanced Calculus II, MTH 312, Oregon State
        University
- Probability III, MTH 465/565, Oregon State
        University
 
- 
      Winter 2023
      
        - Probability II, MTH 464/564, Oregon State
        University
 
- 
      Fall 2021
      
        - Numerical methods, MAT 321/APC 321, Princeton
        University
 
- 
      Fall 2020
      
        - Numerical methods, MAT 321/APC 321, Princeton
        University
 
- 
      Spring 2021
      
        - Linear Algebra with Applications, MAT 202, Princeton
        University
 
- 
      Fall 2018
      
        - Calculus II, MATH 115, Yale University