Jennifer Ryan

I am a Professor and Division Head of Numerical Analysis, Optimization & Systems Theory in the Department of Mathematics at KTH Royal Institute of Technology. I am an expert in high-order numerical methods for partial differential equations (PDEs), with particular emphasis on discontinuous Galerkin methods, superconvergence, and convolutional filtering. My work bridges mathematical theory, algorithm design, and real-world scientific applications, with the overarching goal of developing reliable, high-accuracy computational tools for complex physical systems.

I earned my PhD in Applied Mathematics from Brown University under the supervision of Chi-Wang Shu, supported by a NASA Graduate Student Research Fellowship. Prior to joining KTH, I held academic positions at the Colorado School of Mines, the University of East Anglia, Delft University of Technology, and Virginia Tech. I was also a Householder Postdoctoral Fellow at Oak Ridge National Laboratory.

My research develops mathematically rigorous and computationally efficient techniques to enhance the accuracy, smoothness, and interpretability of numerical solutions of PDEs. I have made foundational contributions to Smoothness-Increasing Accuracy-Conserving (SIAC) filtering and have worked to advance the theory and practical use of superconvergence in numerical methods. My work spans applications in fluid dynamics, wave propagation, plasma physics, aeroacoustics, and computational relativity.

You can find out more about me on my personal website as well as a presentation of the techniques that I develop here. You can find out more and test out our codes for your applications from my github.

Upcoming events:

• Plenary Lectures:
  • British Applied Math Colloquium (BAMC)
  • Society of Industrial and Applied Mathematics (SIAM) Annual meeting
• Invited Talks:
  • BIRS Workshop: Women in Computational Methods for PDEs
  • ICERM Workshop on Mathematical Analysis and Modeling of Maxwell’s Equations in Complex Media
• Organizer:
  • ICERM Workshop: Advances and Challenges in Computational Partial Differential Equations