Computational Engineering is an exciting discipline of engineering that's relatively new. Its aim is to use computer simulations for modeling and design. In many instances, it can replace costly experiments and allow measurement of quantities that are inaccessible to experimentation. Despite the impressive progress in computer hardware, there remain many exciting research problems to improve the reliability, accuracy and speed of computer models and numerical algorithms. My research is focused on large scale scientific computing with application in protein modeling, acoustics, electromagnetics, and microfluidics. For example, the fast multipole method (which was named one of the Top 10 Algorithms of the Century, along with the Monte-Carlo method, Krylov iteration methods, the Householder matrix decomposition and the fast Fourier transform) is one of my main focus areas. Other exciting projects include creating time integrators for multiscale problems and stochastic differential equations. Along with state-of-the-art numerical algorithms, I also have projects in computer science where we use processors developed for gaming by NVIDIA and AMD for scientific computing. These processors can provide unprecedented performance; in some cases, we were able to measure 100x speed-ups between an Intel core and a graphics-processing unit (GPU). In 2008, modern GPUs had hundreds of computing cores and can achieve 1 Teraflop (e.g., 1,000,000,000,000 arithmetic operations per second). Today's programming environments, however, are inadequate for these new parallel processors and this is therefore the focus of several research projects.