Rate My Professor Nathan Ross

Nathan Ross is an Associate Professor in the School of Mathematics and Statistics, Faculty of Science, at the University of Melbourne. He holds a PhD in Mathematics from the University of Southern California, where he was supervised by Jason Fulman, and a Bachelor's Degree from the University of Pittsburgh. Before joining the University of Melbourne in 2013, Ross was a postdoctoral researcher in the Department of Statistics at the University of California, Berkeley, from 2009 to 2013. His research centers on probability theory, stochastic processes, and random discrete structures. He studies the asymptotic behavior of models central to statistical applications, such as random networks, urn models, and gene genealogies. Ross employs and develops Stein's method to derive precise approximation and limit theorems for these large-scale structures. He is a member of the Stochastic Processes research group in his school and contributes to the Probability Victoria community, which promotes probability research and activities in Melbourne.

Ross has authored numerous influential publications in leading probability journals. His widely cited tutorial, 'Fundamentals of Stein’s method,' appeared in Probability Surveys in 2011. Key papers include 'Degree asymptotics with rates for preferential attachment random graphs' with E.A. Peköz and A. Röllin in Annals of Applied Probability (2013); 'Total variation error bounds for geometric approximation' in Bernoulli (2013); 'Shotgun assembly of labeled graphs' with E. Mossel in IEEE Transactions on Network Science and Engineering (2017); 'Joint degree distributions of preferential attachment random graphs' with E. Peköz and A. Röllin in Advances in Applied Probability (2017); 'Generalized gamma approximation with rates for urns, walks and trees' with E.A. Peköz and A. Röllin in Annals of Probability (2016); and 'When do wireless network signals appear Poisson?' with H.P. Keeler and A. Xia in Bernoulli (2018). He served as a co-chief investigator on the Australian Research Council Discovery Project DP150101459, 'Limit Theorems for Probability Models Using Distributional Transformation Fixed Points,' alongside Aihua Xia, Andrew Barbour, and Philip Pollett.