Scientific Computing
Scientific Computing: Symbolic computation, numerical methods and analysis, computer algebra systems, geometric and algebraic algorithms.
Scientific Computing is the study of algorithms and their implementations for computations involving data or mathematical symbols as well as numbers. Our Scientific Computing Group has contributed to the development of Maple, LAMMPS and R, software packages used in various areas of pure and applied mathematics, and statistics.
If you are interested in graduate work in this research area, direct your application to the Department of Applied Mathematics.
- Rob Corless - Symbolic computation, numerical methods and analysis, reliability of numerical methods for dynamical systems.
- Colin Denniston - Soft condensed matter physics, complex fluids, granular matter, theory and simulation of micro and nano-fluidic devices, developing numerical methods and analytical techniques to study the above.
- David Jeffrey - Microhydrodynamics, low Reynolds number flow, lubrication, suspensions of particles, flows in industrial machinery, computer algebra systems.
- Mikko Karttunen - Computational chemistry & biological physics, Multiscale simulation methods, QM/MM and coarse-graining Lattice Boltzmann methods, Polymers, Intrinsically disordered proteins, Lipid membranes and peptides
- Allan B. Maclsaac - Nanomagnetism, Ultra-thin magnetic films, Phase Transitions and Critical Phenomena, Statistical Mechanics, Computer Simulation, Monte Carlo Simulation, Cellular Automata
- Greg Reid - Nonlinear differential equations (especially PDE), algorithms (especially for computer algebra), geometric and algebraic algorithms.
