Our research and teaching activities focus on mathematical problems that arise in realworld applications. This involves the mathematical modeling of physical, biological, medical, and social phenomena, as well as the effective use of current and future computing resources for simulation, computation, data analysis, and visualization. Key areas of research in our group are the modeling of biofilms and of materials, computational neuroscience, traffic flow modeling and simulation, the numerical approximation of differential equations, and the solution of large systems of equations. The faculty and students in our group conduct research on many crossdisciplinary projects, including collaborations with biology, medicine, computer science, and mechanical, electrical, and nuclear engineering. Students conducting research on the mathematical modeling of realworld phenomena and the design of modern computational approaches receive a broad education and training in differential equations, computational mathematics, fluid dynamics, applied analysis, and specialized courses on topics like computational neuroscience, calculus of variations, kinetic equations, and other areas. Hands on research opportunities with modern hardware are provided by the Center for Computational Mathematics and Modeling.
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Former Faculty, Postdocs, and LongTerm Visitors  Former Ph.D. Students 


For more information please consult the department's listing of recent Ph.D. graduates. 
Applied Mathematics  Scientific Computing 



Reflecting the status in November 2010.
Applications to become a Ph.D. student are processed by the Department of Mathematics. Information of about the graduate program in Mathematics can be found on the Graduate Program website. Active Ph.D. students interested in Applied and Computational Mathematics are encouraged to approach the faculty listed above to discuss possible research and coursework directions. Examples for Ph.D. projects are provided in this list of completed a Ph.D. theses.
Students who are interested in obtaining graduatelevel expertise and training in Applied and Computational Mathematics can also achieve a M.S. in Mathematics with Applied Concentration. A M.S. degree can conclude with a M.S. thesis research project.
Examples of suitable courses, for both Ph.D. and M.S. students are listed below (more details in the Course Syllabi Listing).
Some courses are not taught every semester. Please check the website of the Department of Mathematics for the course schedule.
5043/5044. Introduction to Numerical Analysis I / II provides the basis in numerical analysis and fundamental numerical methods, and well as expertise in numerical methods for ordinary differential equations.
8007/8008. Introduction to Methods in Applied Mathematics I / II provides the student with the toolbox of an applied mathematician: derivation of PDE, solution methods in special domains, calculus of variations, control theory, dynamical systems, anymptotic analysis, hyperbolic conservation laws.
8013/8014. Numerical Linear Algebra I / II cover modern concepts and methods to solve linear systems and eigenvalue problems.
8023/8024. Numerical Differential Equations I / II present modern methods for the numerical solution of partial differential equations, their analysis, and their practical application.
8107. Mathematical Modeling for Science, Engineering, and Industry. See above for the description of this special course.
9200/9210. Topics in Numerical Analysis I / II are special courses in Numerical Analysis that focus on topics relating to our group members' active research areas. Examples:
8200/8210. Topics in Applied Mathematics I / II are special courses that are offered by demand. Examples:
In addition, the following courses can provide fundamental theoretical background.
8141/8142. Partial Differential Equations I / II provide a theoretical understanding of many of the equations considered in 8023/8024.
9005. Combinatorial Mathematics relates to many key problems in Scientific Computing, such as mesh generation, load balancing, and multigrid.9041. Functional Analysis is a theoretical basis for many numerical approximation approaches, such as the finite element method.
9043. Calculus of Variations provides powerful tools for the theoretical study of dynamics, structural mechanics, and material properties.