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Dong ZhouView profile

Associate Professor

• Numerical Analysis
• Computational Mathematics
• Partial Differential Equations
• +3 more

Dong Zhou is an Associate Professor in the Department of Mathematics at California State University, Los Angeles within the College of Natural and Social Sciences, a position held since August 2024. Previously, he served as Assistant Professor at the same institution (2018-2024), Research Assistant Professor at Temple University (2014-2018), and Visiting Scientist at MIT (2015). His educational background includes: Ph.D. in Applied Mathematics, Temple University (2014) B.S. in Mathematics, Beijing Normal University (2008) Zhou's research focuses on developing high-order numerical methods for partial differential equations in computational fluid dynamics and scientific computing. His work addresses critical challenges in fluid flow and interface propagation, with major contributions in overcoming order reduction in Runge-Kutta methods through weak stage order theory and modified boundary conditions, and establishing unconditional stability frameworks for implicit-explicit multistep schemes. His methodologies enable time-step selection based purely on accuracy requirements for stiff systems. Analysis of his publication record reveals consistent innovation in numerical time-integration techniques, with recent work (2021-2024) emphasizing practical implementations for incompressible flow problems using pressure Poisson equation reformulations. His research bridges theoretical stability analysis with applications in engineering contexts requiring high-order spatial and temporal accuracy. Scientific Awards: None documented in source materials. Grant activity includes serving as Co-PI on NSF grant DMS-1719640 (2017-2020) titled "Collaborative Research: Overcoming order reduction and stability restrictions in high-order time-stepping," working with Benjamin Seibold (Temple University), Rodolfo Rosales (MIT), and David Shirokoff (NJIT). No student advising information was provided. His research is conducted through institutional collaborations with Temple University, MIT, and NJIT, though no dedicated laboratory structure is specified in available documentation.

Ruiwen ShuView profile

Assistant Professor

• Energy Minimization
• Potential Theory
• Gradient Flows
• +4 more

Ruiwen Shu is an Assistant Professor in the Department of Mathematics at the University of Georgia. His research focuses on energy minimization, potential theory, gradient flows, collective dynamics, and numerical methods for kinetic and hyperbolic problems. Education: Ph.D. in Mathematics, University of Wisconsin-Madison (2014–2018) Postdoctoral Fellow, University of Maryland (2018–2021) Postdoctoral Research Associate, University of Oxford (2021–2022) Research Interests: Dr. Shu investigates mathematical models of collective behavior, including anisotropic interaction energies, aggregation-diffusion systems, and hydrodynamic limits. His work bridges theoretical analysis with computational methods, developing high-accuracy numerical schemes for stiff PDEs and kinetic equations. Key themes include pattern formation in repulsive-attractive systems, stability of minimization algorithms, and asymptotic-preserving discretizations. Publication Trends: Recent articles emphasize anisotropic interaction energies (2023–2024), structure-preserving numerical methods for kinetic/hyperbolic systems (2021–2025), and stability analysis of aggregation phenomena. Computational techniques like IMEX Runge-Kutta and semi-Lagrangian schemes are recurrent, applied to models ranging from Euler-Poisson equations to BGK kinetics.

Daniel R. ReynoldsView profile

Professor

• Scientific Computation
• Time Integration Methods
• Parallel Algorithms
• +2 more

Daniel R. Reynolds is a Professor at Southern Methodist University (joining UMBC in 2025) specializing in large-scale scientific computation. He develops mathematically rigorous algorithms for multiphysics problems in climate science, fusion energy, cosmology, and materials science. His research creates parallel time integration methods and algebraic solvers that ensure accuracy, stability, and scalability for complex simulations, bridging mathematical theory with computational practice. His publications consistently advance time integration techniques for differential-algebraic systems, with recent work emphasizing multirate methods for tokamak plasma turbulence and Earth system models. He leads software development for the SUNDIALS suite and collaborates on DOE/NSF-funded projects including 'Adaptive Multirate Time Integration in the ARKODE Library'. Reynolds teaches graduate courses in computational mathematics and mentors PhD students in algorithm development.

• Scientific Computing
• Numerical Methods for Partial Differential Equations
• Galerkin Methods
• +3 more

Frank Giraldo is a Distinguished Professor of Applied Mathematics at the Naval Postgraduate School (NPS) and an Adjunct Professor of Applied Mathematics at the University of California, Santa Cruz. He leads the Scientific Computing group at NPS, focusing on numerical methods for partial differential equations (PDEs), particularly in atmospheric and oceanic systems. His research emphasizes discontinuous and continuous Galerkin methods, high-performance computing (HPC), and large-eddy simulations (LES) for modeling geophysical flows. Education: PhD in Applied Mathematics from the University of Virginia (1994), BSE from Princeton University (1987). Research Interests: Nonhydrostatic atmospheric modeling (NUMA), ocean modeling (NUMO), LES of hurricanes and fluid dynamics, scalable algorithms for supercomputers (e.g., GPU acceleration), and implicit-explicit time-integration methods. His work integrates HPC with climate science and has been applied to the U.S. Navy's NEPTUNE system. Awards: Menneken Award (2025), NPS Distinguished Professor (2020), Carl E. Menneken Award (2007), Alan Berman Research Prize (2001). Leadership: Chair of NPS Applied Mathematics Department (2021–2025), member of the U.S. Interagency Council for Advancing Meteorological Services (ICAMS), and scientific advisor to international programs like the German Research Foundation's Geophysical Fluid Dynamics initiative. Labs/Teams: Principal developer of the NUMA atmospheric model and NUMO ocean model. Collaborates with institutions like NJIT, Hampton University, and Lawrence Livermore National Lab on projects funded by ONR, DARPA, and NSF.

• Hydrodynamic Modeling
• Oceanography
• Fluid Mechanics
• +3 more

Eric Deleersnijder is a Lecturer at the Université catholique de Louvain, affiliated with the Louvain Polytechnic School (EPL) and the Institute of Mechanics, Materials and Civil Engineering (iMMC). He conducts research within the Applied Mechanics and Mathematics (MEMA) unit of iMMC and collaborates with the Earth and Life Institute (ELI). His teaching responsibilities include courses such as General Mechanics, Marine Hydrodynamics, Fundamentals of Geographic and Environmental Modelling, Mathematical Ecology, Fluid Mechanics II, and Physics of Fluids. Deleersnijder's research focuses on hydrodynamic modeling , coastal and estuarine dynamics , and numerical methods for environmental systems. Key areas include: multi-scale ocean modeling, water renewal timescales, sediment transport, and contaminant dispersion in aquatic systems. His work emphasizes practical applications in ecosystems like the Scheldt Estuary, Nador Lagoon (Morocco), and Meuse River, alongside theoretical advances in finite volume/discontinuous Galerkin methods. Analysis of his recent publications reveals a strong emphasis on: Development of advanced numerical schemes (IMEX Runge-Kutta, DG-FEM) for 3D hydrodynamic systems Environmental risk modeling of radionuclide dispersion in river networks Hydrodynamic diagnostics (water age, residence time) for coastal management Transdisciplinary applications from Titan's subsurface oceans to North African lagoons He actively collaborates on continental-scale hydrological projects (e.g., Scheldt and Meuse river systems) and leads developments in the SLIM (Second-generation Louvain-la-Neuve Ice-ocean Model) platform. His team addresses climate impacts on water renewal and sediment dynamics through high-performance computing approaches.