Jonathan Christopher Mattingly
- James B. Duke Distinguished Professor
- Professor of Mathematics
- Chair of the Department of Mathematics
Research Areas and Keywords
Stochastic Analysis, Malliavin Calculus, Ergodic Theory
Stochastic and Random PDEs, Stochastic Dynamical Systems, Mathematical Ecology and Evolution, Metabolic and Cellular modeling, Out of equilibrium statistical mechanics
Markov Chain Mixing, Stochastic Numerical Methods, High Dimensional Random Algorithms
PDE & Dynamical Systems
Stochastic and Random PDEs, Stochastic Dynamical Systems, Malliavin Calculus, Fluid Mechanics, Approximating invariant measures
Stochastic and Random PDEs, Stochastic Dynamical Systems, Fluid Mechanics
Stochastic and Random PDEs, Stochastic Dynamical Systems, Stochastic Analysis, Malliavin Calculus, Markov Chain Mixing, Ergodic Theory, High Dimensional Random Algorithms, Probability on stratified spaces, Out of equilibrium statistical mechanics, Approximating invariant measures
Jonathan Christopher Mattingly grew up in Charlotte, NC where he attended Irwin Ave elementary and Charlotte Country Day. He graduated from the NC School of Science and Mathematics and received a BS is Applied Mathematics with a concentration in physics from Yale University. After two years abroad with a year spent at ENS Lyon studying nonlinear and statistical physics on a Rotary Fellowship, he returned to the US to attend Princeton University where he obtained a PhD in Applied and Computational Mathematics in 1998. After 4 years as a Szego assistant professor at Stanford University and a year as a member of the IAS in Princeton, he moved to Duke in 2003. He is currently a Professor of Mathematics and of Statistical Science.
His expertise is in the longtime behavior of stochastic system including randomly forced fluid dynamics, turbulence, stochastic algorithms used in molecular dynamics and Bayesian sampling, and stochasticity in biochemical networks.
He is the recipient of a Sloan Fellowship and a PECASE CAREER award. He is also a fellow of the IMS and the AMS.
HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation (Senior Investigator). 2019 to 2022
Collaborative research: Propagation of dissipation: Stochastic stabilization in finite and infinite dimensions awarded by National Science Foundation (Principal Investigator). 2016 to 2020
Quantifying Gerrymandering in the North Carolina Legislature awarded by (Principal Investigator). 2019
BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees awarded by National Science Foundation (Co-Principal Investigator). 2015 to 2019
Analysis and design of robust rare event simulation methods for protein folding and d awarded by University of Chicago (Principal Investigator). 2013 to 2018
Southeastern Probability Conference 2017: Special Edition Interacting Particle Systems with Applications in Biology, Ecology, and Statistical Physics awarded by National Science Foundation (Principal Investigator). 2017 to 2018
EMSW21-RTG: awarded by National Science Foundation (Co-Principal Investigator). 2010 to 2017
Multiscale Analysis of Dynamic Graphs awarded by Office of Naval Research (Co-Principal Investigator). 2012 to 2016
FRG: Collaborative Proposal: awarded by National Science Foundation (Principal Investigator). 2009 to 2013
CAREER: Stochastic Analysis and Numerics in Partial Differential Equations and Extended Dynamical Systems awarded by National Science Foundation (Principal Investigator). 2005 to 2011
Herzog, David P., and Jonathan C. Mattingly. “Ergodicity and Lyapunov Functions for Langevin Dynamics with Singular Potentials.” Communications on Pure and Applied Mathematics, vol. 72, no. 10, WILEY, Oct. 2019, pp. 2231–55. Wos, doi:10.1002/cpa.21862. Full Text Open Access Copy
Chin, Andrew, et al. “The Signature of Gerrymandering in Rucho v. Common Cause.” South Carolina Law Review, vol. 70, 2019.
Bakhtin, Y., et al. Smooth invariant densities for random switching on the torus. Aug. 2017. Open Access Copy
Johndrow, J. E., and J. C. Mattingly. Coupling and Decoupling to bound an approximating Markov Chain. July 2017. Open Access Copy
Glatt-Holtz, N. E., et al. “Scaling and Saturation in Infinite-Dimensional Control Problems with Applications to Stochastic Partial Differential Equations.” Annals of Pde, June 2017. Open Access Copy
Glatt-Holtz, N., et al. “On Unique Ergodicity in Nonlinear Stochastic Partial Differential Equations.” Journal of Statistical Physics, vol. 166, no. 3–4, Feb. 2017, pp. 618–49. Scopus, doi:10.1007/s10955-016-1605-x. Full Text Open Access Copy
Cooke, B., et al. “Geometric ergodicity of two-dimensional hamiltonian systems with a Lennard-Jones-like repulsive potential.” Communications in Mathematical Sciences, vol. 15, no. 7, Jan. 2017, pp. 1987–2025. Scopus, doi:10.4310/CMS.2017.v15.n7.a10. Full Text Open Access Copy
Hairer, M., and J. Mattingly. The strong Feller property for singular stochastic PDEs. 2016. Open Access Copy
Carter, Daniel, et al. Optimal Legislative County Clustering in North Carolina.
Carter, Daniel, et al. A Merge-Split Proposal for Reversible Monte Carlo Markov Chain Sampling of Redistricting Plans.
Mathematics Department chair Jonathan Mattingly has been awarded the James B. Duke distinguished professorship. Distinguished professorships recognize both exceptional achievement and the potential for future accomplishments. They are awarded to... read more »
DURHAM, N.C. -- On March 26, the U.S. Supreme Court is set to hear a North Carolina lawsuit that could end partisan gerrymandering for good -- and a mode of analysis developed at Duke University could impact their decision. The nation’s highest... read more »
On January 9th, a three-judge court declared North Carolina’s congressional map unconstitutionally gerrymandered, stating that the drawing of the state’s electoral districts gave an advantage to the Republican Party. The US Federal court cited the... read more »