Center for Applied Mathematics and Statistics (CAMS) Main Research Areas

CAMS members conduct research in a broad range of areas of applied mathematics, using, in particular, the techniques of scientific computation/numerical analysis, applied analysis and analysis of PDEs, mathematical modeling and asymptotics, dynamical systems theory, and statistics. The focus of CAMS research is roughly divided into five application areas (with significant overlap), listed below.

Mathematical Biology

Experimental, Computational and Mathematical Neuroscience

  • Neuronal dynamics, synaptic plasticity, neuronal networks
  • Electrical activity in excitable cells, calcium dynamics
  • Interaction between electrical dynamics and gene expression
  • Applications to visual cortex, central pattern generators, spreading cortical depression and circadian rhythms


Biological Fluid Dynamics

  • Immersed interface/continuum methods
  • Biomechanics and tissue engineering
  • Cell and vesicle dynamics


Developmental Biology

  • Morphogenetic patterning, embryogenesis


Bose, Bukiet, Cummings, Diekman, Golowasch, Matveev, Miura, Muratov, Nadim, Rotstein, Young (External Members: Booth, Bunker, Holzapfel, Huang, Russel, Tao, Wylie).

Calculated oxygen distributions in the tissue surrounding three-dimensional arrays of capillaries

Fluid Dynamics

Interfacial Flows

  • Fast/multiscale numerical methods
  • Analysis and simulation of jet breakup, bubble dynamics, surfactant effects
  • Modeling, analysis and simulation of thin films and wetting phenomena
  • Fluid-structure interaction
  • Electrohydrodynamics and electrokinetics
  • Geophysical fluid dynamics


Multiphase and Reactive Flows

  • Flame-flow interaction, droplet burning, ignition
  • Cryogenic fluids for rocket propulsion technology
  • Micro- and nano-fluidics
  • Complex fluids


Numerical Methods and PDE Analysis

  • Boundary integral, volume of fluid, level set methods
  • Convergence and error estimates for numerical methods
  • Fast/multiscale methods
  • Well-posedness for interfacial flows
  • Analysis of singularity formation


Afkhami, Bechtold, Blackmore, Booty, Bukiet, Choi, Cummings, Jiang, Kondic, Luke, Muratov, Petropoulos, Siegel, Young (External Members: Diez, Huang, Meegoda, Papageorgiou, Rosato, Vanden-Broeck, Wylie).

Numerical simulation of the kinetics of droplet formation in polymer-liquid crystal mixtures

Linear and Nonlinear Waves

Underwater Acoustics and Ocean Waves

  • Inverse scattering, matched field processing, target detection and localization
  • Surface and internal waves
  • Data assimilation



  • Finite element and domain decomposition methods
  • Fast algorithms, fast multipole and related methods
  • Effective media, homogenization, dispersive media
  • Scattering from complex objects


Nonlinear Optics

  • Ultrafast optical communications
  • Solitons in optical fibers


Front Propagation

  • Traveling waves in reaction-diffusion systems
  • Population dynamics
  • Flame dynamics and detonation


Ahluwalia, Bechtold, Boubendir, Choi, Goodman, Jiang, Kriegsmann, Michalopoulou, Moore, Muratov, Petropoulos, Russel, Turc (External Member: Erneux, Papageorgiou, Vanden-Broeck).

Ambiguity surface generated by matched field processing of an exhaustive search for unknown source range and depth

Materials science


Soft Matter

  • Molecular dynamics simulations of granular materials
  • Modeling and asymptotics for liquid crystal systems
  • Mesoscopic phases in polymer systems
  • Biomaterials


Energy Driven Pattern Formation

  • Analysis of variational problems with long-range forces
  • Modeling, analysis and simulation of magnetic systems
  • Numerical methods for interfacial phenomena in solids
  • Large deviations, rare events and nucleation phenomena


Afkhami, Blackmore, Booty, Cummings, Kondic, Muratov, Siegel, Young (External Members: Rosato, Wylie).

Mesoscopic Model for Pattern Formation in Materials



  • Multivariate methods
  • Clinical trials and Longitudinal Data Analysis
  • Microarray and Proteomics analysis
  • Generalized Additive Models and Semi-parametric methods


High dimensional data

  • Multiple testing and False Discovery Rate
  • Analysis of massive datasets
  • Variable selection


Spatial statistics

  • Analysis of spatial point patterns
  • Spatial anomaly detection


Survival Analysis

  • Copulas and competing risk models
  • Bootstrap of survival data
  • Empirical likelihood
  • High dimensional censored data analysis
  • Reliability models


Dhar, Guo, Loh, Subramanian, Wang (External Members: Johnson, Sverdlove).

The Mean Remaining Life function MRL(x) ≡ E(X-x | X > x)