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Applied Mathematics

My first area of research was classical general relativity, and in particular (quasilocal) notions of gravitational energy and momentum. Over the last 15 years, I've migrated towards solution of PDE via spectral-element methods, and have written a large code for solving the Einstein equations reduced by helical symmetry. This work remains unfinished, as I'm struggling with issues of boundary conditions and gauge. In short, the code solves the helically reduced Einstein equations in the harmonic (or de Donder) gauge. However, the boundary conditions I'm currently using violate this gauge, as do the computed solutions (so they are not, strictly speaking, solutions to the Einstein equations). Possibly through the use of spectral penalty methods, I hope to implement better boundary conditions soon.

Applied Mathematics

The primary aim of my research program is to develop and analyze mathematical models and computational methods for problems at the intersection of fluid dynamics, biology, and transport in multi-phase media. Through mathematical models, my work elucidates the physical processes necessary for biological function. In my opinion, a model is only as good as the assumptions underpinning it and must respect the physics that impact the system of interest. Non-Newtonian rheology, immersed elastic structures, and electro-diffusion all play critical roles in the biological systems I study. A modeling approach that incorporates these non-linearities (or other complex effects) often results in mathematical equations which have not been previously studied. Analyzing such models requires the use of new or novel mathematical techniques. Consequently, my work typically requires the solution and analysis of partial differential equations via PDE theory, clever application of perturbation analysis, or the development and use of numerical methods.

At small scales, biological function is a consequence of the laws of physics, and the laws of physics are written in the language of mathematics. Individual cells control the transport of dissolved species in coordinated ways, respond to diffuse chemical signals, store chemical energy, and later expend it to perform mechanical work. Larger collections of cells work together to manipulate the distribution of ions within the body, secrete enzymes and other proteins, or generate the forces and deformations necessary to drive larger scale transport through organs. All of these processes are carried out in heterogeneous and dynamic environments. Mathematical modeling (particularly when used in collaboration with the work of experimentalists) is an essential tool that allows one to address important and complex questions about the physical principles underlying the function of living systems, often providing insights that are simply not accessible through experimental techniques. In my experience, such an approach leads to mathematical problems, analyses, and techniques that are interesting in their own right.

Jens Lorenz
Applied Mathematics

My areas of reseach are Applied Analysis and Scientific Computing. Specific problems that I have worked on lately include: the stability of shock and spiral waves; the modeling of ocean circulations for climate studies; the transition from smooth to rough attractors and Lyapunov-type numbers.

I am broadly interested in partial differential equations and their numerical approximation. Most of the applications I have worked on are related to fluids, but a recent interest is a nonlinear Black-Scholes equation in financial mathematics. The Black-Scholes equation is used extensively for option pricing; it becomes nonlinear if one assumes dependence of the volatility on the asset price.

Terry Loring
Pure Mathematics

My current research is a blend of operator theory, C*-algebras, K-theory, numerical algorithms, mostly aimed at better computer modeling of of topological phases such as topological metals, higher order topological insulators.  Some of my research is in collaboration with the Nanostructure Physics department at the Center for Integrated Nanotechnologies, Sandia National Laboratories.

More information on my research is listed on the research page of my webpage.

Statistics

My research concentrates on survey sampling, mixed models and nonparametric regression. Much of my work develops new statistical theory and methods in those three areas. I am interested in regression coefficient estimation, partially linear models, lack of fit tests and identification of influential observations in dual frame surveys. My interests also include nonparametric regression for complex surveys, generalized linear mixed models and mixed random forests.

Pavel Lushnikov
Applied Mathematics

My research interests include a wide range of topics in applied mathematics, nonlinear waves and theoretical physics. Among them are laser fusion and laser-plasma interaction; dynamics of fluids with free surface, Kelvin-Helmholtz instability and nonlinear interactions of surface waves; theory of the wave collapse, singularity formation and its application to plasma physics, hydrodynamics, biology and nonlinear optics; bacterial aggregation, chemotaxis, cell-cell interactions; collapse of bacterial colonies, stochastic Potts model of biological cell; pattern formation in photorefractive crystals and other nonlinear optical media; high-bit-rate optical communication; dispersion-managed optical fiber systems; soliton propagation in optical systems; high performance parallel simulations of optical fiber systems; Bose-Einstein condensation of ultracold dipolar gases.

Applied Mathematics

My general research is in predictive computational science, a discipline concerned with assessing the predictability of mathematical and computational models of physical systems. It involves and bridges several fields and procedures such as computational mathematics, uncertainty quantification, and verification and validation. In the past few years, I have been working on the analysis and computation of partial differential equations with uncertain parameters described by stochastic and fuzzy functions. I work on both forward and inverse problems with applications to materials science, earthquake engineering, and high-frequency waves. I am currently developing new mathematical and computation models for the prediction of physical systems which are subject to hybrid random and non-random uncertainties.

 

monike nitsche
Applied Mathematics

I work in the area of computational fluid dynamics. In particular, I have studied vortex dynamics in separated flows, interfacial flows, both  in inviscid flow and in Stokes flow, evolution of internal waves in flows with density gradients, and numerical methods to compute the fluid motion accurately.

 

Dr. Maria Cristina Pereyra
Pure Mathematics

My research area is harmonic analysis, I am interested in the study of Calderon-Zygmund operators, their properties and generalizations, in particular boundedness properties on weighted Lebesgue spaces, the theory of extrapolation and dyadic harmonic analysis. Currently I am working on developing the theory of Hardy and BMO spaces on spaces of homogeneous type.
In the past I have done some research in wavelet theory, specifically building  divergence free wavelets and exploring some of their applications.

Here is a link to my publications .

For more information you can visit my webpage .

Applied Mathematics
The core direction of Jacob's research is numerical analysis and scientific computing. His specific focus is on high-performance computing, iterative solvers for large sparse (non)linear systems, their associated preconditioning, and numerical PDEs. He approaches his research both from a software perspective centered on providing these methods to the broader community and also from a theoretical perspective centered on the development of new methods. His chief software projects are XBraid (Parallel Multigrid Solvers in Time), PyAMG (Algebraic Multigrid Solvers in Python), and hypre (high performance preconditioners). 
 
Areas: Numerical analysis, computational science, high performance computing
 
Keywords: Iterative methods, preconditioning, multigrid, neural networks, numerical PDEs, parallel-in-time
 

For more information, please see my homepage https://math.unm.edu/~schroder/index.html

 

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