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High-Performance Computing

Together with theory and experimentation, computational methods now constitute a third pillar of scientific inquiry. There's no denying the role that technology plays in sustaining scientific leadership and economic competitiveness. Advanced technologies now allow researchers to build and test models of complex phenomena and then manage and analyze almost unimaginably large volumes of data. Stellar explosions, climate shifts, the effects of gene flow on ecological communities, multi-scale earthquake-induced structural stresses, and nuclear fusion cannot be replicated, but they certainly can be simulated.

Typical Graduate Programs
Graduate students interested in high performance computing often pursue degrees such as:
Computational engineering and science are key to developing models of behavior and modes of scientific discovery that enable significant and often cost-effective progress in solving the grand challenges of our time.

Lehigh researchers use computational modeling to help doctors improve cancer-radiation techniques to limit damage to healthy cells around tumors, and to allow people stricken with immobilizing diseases to control devices through thought recognition. Others seek to gauge the effect of earthquakes on interconnected infrastructure systems, or to understand the diffusion of aluminum and oxygen ions in the manufacture of advanced ceramics. Still others intend to improve the effectiveness of search engines on the Web.

Lehigh continues to gather preeminent research faculty from all disciplines of science and engineering to broaden appreciation of the pervasiveness of computational methods in scientific and engineering research, and to explore related challenges and opportunities. Graduate students benefit from these cross-disciplinary initiatives and from access to high-end computational systems and methodologies that make it possible to accomplish critical tasks.
Sustainable Infrastructure

Research Spotlight

Ted Ralphs (above), associate professor of industrial and systems engineering, focuses mainly on various aspects of mixed-integer linear programming. He tackles parallel processing challenges by writing "scalable" algorithms that determine how to move data so each processor optimizes its contribution to the overall computation. Ralphs is a co-founder of the Computational Optimization Research at Lehigh (COR@L) Laboratory, and chairs Lehigh's High Performance Computing Steering Committee.