My research work has mostly been in “scientific computing,” a generic term that refers to computational approaches to problems arising in the natural sciences and engineering. This field combines mathematical modeling of natural and engineered systems, algorithms, advanced software implementation techniques, high-performance parallel computing, computational performance measurement and modeling, and data and visual analytics — all to solve some of the most important problems out there. Great stuff!
I love this quote from Dan Reed (from this 2008 article in SIAM News), which views computational modeling & science as a scientific instrument:
The breadth of these examples highlights a unique aspect of computational modeling that distinguishes it from other scientific instruments—its universality as an intellectual amplifier. Powerful new telescopes advance astronomy, but not materials science. Powerful new particle accelerators advance high-energy physics, but not genetics. In contrast, computing and computational models advance all of science and engineering, because all disciplines benefit from high-resolution model predictions, theoretical validations, and experimental data analysis.