I am a Professor of Mathematics at Rutgers University–Camden and a Visiting Scholar at the Center for Computational Mathematics at the Flatiron Institute. My research focuses on theoretical and computational probability, with an emphasis on Markov chain mixing times and Markov Chain Monte Carlo methods. From 2019 to 2021, I was a Visiting Professor in Probability Theory and Stochastic Analysis at Bonn University. I earned my PhD in Applied and Computational Mathematics from Caltech before holding an NSF Mathematical Sciences Postdoctoral Research Fellowship and a Courant Instructorship at NYU. My work has been supported by multiple NSF grants, and I’ve been fortunate to receive the Rutgers–Camden Chancellor’s Award for Outstanding Research and a Humboldt Research Fellowship for Experienced Researchers.
Below are some selected publications. For a complete list, you can check out my Google Scholar profile. I also engage in discussions on related mathematical topics on MathOverflow. If you’re interested, you can watch my recent talk on the reversibility and mixing time of the No-U-Turn Sampler.
Publications
-
The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler, N. Bou-Rabee, B. Carpenter, T. S. Kleppe & S. Liu, 2026
Journal of Machine Learning Research 27(113), 1-64 -
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo, N. Bou-Rabee, B. Carpenter & M. Marsden, 2026
Statist. Surv. 20: 135-179 (2026). -
Accelerated Convergence in Hit-and-Run Monte Carlo and a Coordinate-free Randomized Kaczmarz Algorithm, N. Bou-Rabee, A. Eberle, & S. Oberdörster, October 2025
Electronic Journal of Probability, Vol. 30, paper no. 154, 1-28. -
Unadjusted Hamiltonian MCMC with Stratified Monte Carlo Time Integration, N. Bou-Rabee & M. Marsden, 2025
Annals of Applied Probability, Vol. 35, No. 1, 360-392. -
Mixing Time Guarantees for Unadjusted Hamiltonian Monte Carlo, N. Bou-Rabee & A. Eberle, 2023
Bernoulli, Volume 29, Issue 1, pages 75-104