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Contact


Email

nathan.ross@unimelb.edu.au

Credentials


Position
Associate Professor
School of Mathematics and Statistics
Education
PhD
University of Southern California
Bachelors Degree
University of Pittsburgh-Pittsburgh Campus
ORCID

0000-0002-4097-7065

A/Prof Nathan Ross

Associate Professor
School of Mathematics and Statistics

39 Scholarly works
2 Projects

HIGHLIGHTS

  • 2025

    Journal article

    Detection of Unknown RF Signals Using Rydberg-Atom Quantum MIMO Receivers
    DOI: 10.1109/LCOMM.2025.3599481
  • 2025

    Journal article

    Cutoff for the logistic SIS epidemic model with self-infection
    DOI: 10.1017/apr.2025.16
  • 2025

    Conference Proceedings

    Dynamic Scheduling for Enhanced Performance in RIS-assisted Cooperative Network with Interference
    DOI: 10.1109/VTC2025-Fall65116.2025.11310061
  • 2024

    Journal article

    Gaussian random field approximation via Stein's method with applications to wide random neural networks
    DOI: 10.1016/j.acha.2024.101668
  • 2024

    Journal article

    Stein’s method, smoothing and functional approximation
    DOI: 10.1214/24-EJP1081
  • 2023

    Journal article

    STEIN’S METHOD, GAUSSIAN PROCESSES AND PALM MEASURES, WITH APPLICATIONS TO QUEUEING
    DOI: 10.1214/22-AAP1908
  • 2021

    Journal article

    Stein’s method for the poisson–dirichlet distribution and the ewens sampling formula, with applications to Wright–Fisher models
    DOI: 10.1214/20-AAP1600
Nathan Ross

RECENT SCHOLARLY WORKS

  • 2020

    Journal article

    Local limit theorems for occupancy models
    DOI: 10.1002/rsa.20967
  • 2020

    Journal article

    The probability of intransitivity in dice and close elections
    DOI: 10.1007/s00440-020-00994-7
  • 2020

    Journal article

    Exponential and laplace approximation for occupation statistics of branching random walk
    DOI: 10.1214/20-EJP461

RECENT PROJECTS

  • 2015

    Research Grant

    Random Discrete Structures: Approximations and Applications
  • 2014

    Internal Research Grant

    Limit Theorems for Probability Models Using Distributional Transformation Fixed Points

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