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 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
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The No-Underrun Sampler: A Locally-Adaptive, Gradient-Free MCMC Method, N. Bou-Rabee, B. Carpenter, S. Liu, & S. Oberdörster, January 2025
arXiv:2501.18548 -
Ballistic Convergence in Hit-and-Run Monte Carlo and a Coordinate-free Randomized Kaczmarz Algorithm, N. Bou-Rabee, A. Eberle, & S. Oberdörster, December 2024
arXiv:2412.07643 -
Mixing of the No-U-Turn Sampler and the Geometry of Gaussian Concentration, N. Bou-Rabee & S. Oberdörster, October 2024
arXiv:2410.06978 -
Incorporating Local Step-Size Adaptivity into the No-U-Turn Sampler using Gibbs Self Tuning, N. Bou-Rabee, B. Carpenter, T. S. Kleppe & M. Marsden, August 2024
arXiv:2408.08259 -
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo, N. Bou-Rabee, B. Carpenter & M. Marsden, April 2024
arXiv:2404.15253 -
Randomized Runge-Kutta-Nyström Methods for Unadjusted Hamiltonian and Kinetic Langevin Monte Carlo , N. Bou-Rabee & T. S. Kleppe, 2025
Mathematics of Computation, electronically published on February 4, 2025 (to appear in print). -
Unadjusted Hamiltonian MCMC with Stratified Monte Carlo Time Integration, N. Bou-Rabee & M. Marsden, 2025
Annals of Applied Probability 2025, Vol. 35, No. 1, 360-392. -
Nonlinear Hamiltonian Monte Carlo & its Particle Approximation, N. Bou-Rabee & K. Schuh, 2023
arXiv:2308.11491 -
Mixing of Metropolis-Adjusted Markov Chains via Couplings: The High Acceptance Regime, N. Bou-Rabee & S. Oberdörster, 2024
Electronic Journal of Probability, Volume 29, pages 1 - 27 -
Mixing Time Guarantees for Unadjusted Hamiltonian Monte Carlo, N. Bou-Rabee & A. Eberle, 2023
Bernoulli, Volume 29, Issue 1, pages 75-104 -
Convergence of unadjusted Hamiltonian Monte Carlo for mean-field models, N. Bou-Rabee & K. Schuh, 2023
Electronic Journal of Probability, Volume 28, pages 1 - 40 -
Couplings for Andersen Dynamics, N. Bou-Rabee & A. Eberle, 2022
Ann. Inst. H. Poincaré Probab. Statist, Vol. 58, No.2, pp. 916-944 -
Two-scale coupling for preconditioned Hamiltonian Monte Carlo in infinite dimensions, N. Bou-Rabee & A. Eberle, 2020
Stoch PDE: Anal Comp, Volume 9, pp. 207-242 -
Coupling and Convergence for Hamiltonian Monte Carlo, N. Bou-Rabee, A. Eberle & R. Zimmer, 2020
Ann. Appl. Probab., Volume 30, Number 3, pp. 1209-1250. -
Sticky Brownian Motion and its Numerical Solution, Nawaf Bou-Rabee & Miranda Holmes-Cerfon, 2020
SIAM Review, Vol. 62, No. 1, pp. 164-195. -
Geometric Integrators and the Hamiltonian Monte Carlo Method, N. Bou-Rabee & J. M. Sanz-Serna, 2018
Acta Numerica, Vol. 27, pp. 113-206. -
Continuous-Time Random Walks for the Numerical Solution of Stochastic Differential Equations , N. Bou-Rabee & E. Vanden-Eijnden, 2018
Memoirs of the American Mathematical Society, Volume 256 -
Randomized Hamiltonian Monte Carlo,, N. Bou-Rabee & J. M. Sanz-Serna, 2017
Ann. Appl. Probab. Volume 27, Number 4, pp. 2159-2194