معرفی
Jonathan Christopher Mattingly is the Kimberly J. Jenkins Distinguished University Professor of New Technologies at Duke University, holding professorships in Mathematics and Statistical Science within Trinity College of Arts & Sciences. His research spans stochastic dynamics, fluid dynamics, and quantifying gerrymandering, with notable contributions to stochastic partial differential equations and redistricting analysis. He earned his BS from Yale University and PhD from Princeton University under Yakov Sinai. Mattingly has held roles at Stanford University and the Institute for Advanced Study before joining Duke in 2003. He is a fellow of the American Mathematical Society and the Institute of Mathematical Statistics, and recipient of the PECASE and Sloan Fellowship. His work on gerrymandering has influenced legal cases, including Rucho v. Common Cause.
Education
- B.S. in Applied Mathematics with Physics concentration, Yale University (1992)
- PhD in Applied and Computational Mathematics, Princeton University (1998)
Research Interests
Mattingly’s work focuses on long-time behavior of random dynamical systems, stochastic PDEs, and applications to fluid dynamics and biological systems. He pioneered methods to mathematically assess gerrymandering using ensemble analysis of districting maps. Recent projects include studying enhanced dissipation in fluid flows and central limit theorems on stratified spaces.
Grants & Awards
- National Science Foundation RTG Grant (2021–2026)
- Simons Foundation Stochastic Dynamics Grant (2024–2025)
- Defender of Freedom Award (Common Cause, 2018)
Advising & Contributions
Mattingly mentors graduate and undergraduate researchers, with notable alumni advancing in academia and industry. His lab collaborates on redistricting software tools and stochastic modeling. Current projects include quantifying partisan bias in North Carolina’s legislative maps and analyzing mixing rates in fluid systems.
Labs & Collaborations
He leads the Duke Quantifying Gerrymandering group, developing computational methods to evaluate electoral fairness. Collaborates with statisticians and legal experts on high-impact cases, integrating mathematical rigor into public policy debates.
