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Modeling, simulation, high-end computing and data analysis, for information-based knowledge discovery.
Combining engineering methods with molecular biology, leading to synthesis of new functional materials, molecular machines, and therapeutics.
A multidisciplinary and holistic view of the living systems that moves beyond molecular link scales to understand biological complexity at multiple levels.
CLS in the context of Emory's Strategic Plan.
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Computational Science & Informatics

Modeling, simulation, high-end computing and data analysis, for information-based knowledge discovery.

Computational Science and Informatics forms one of the three pillars of the Computational and Life Sciences Strategic Initiative at Emory. Computation and information-based knowledge discovery have emerged as mainstream scientific methodologies, and are pervasive in every field of study. The CS & I pillar will serve CLS as an enabling arm by facilitating computationally-driven advances in Synthetic Sciences and Systems Biology, while simultaneously advancing the intrinsic disciplines of Computational Science and Informatics.


Computational Science has traditionally been associated with numerical simulations, and has been a mainstay of fields such as high-energy physics, fluid mechanics and molecular modeling. It is typically dependent on very high-performance computing or “number crunching”, and draws heavily on numerical analysis, matrix algorithms, and differential equations. Informatics can be viewed as the data-oriented complement and counterpart of computational science. The term is traditionally associated with the processing and analysis of very large quantities of information with (relatively) less emphasis on number-crunching. Establishing and discovering relationships within data, inferring meaning, searching for patterns, and knowledge discovery through data “mining” are examples of procedures that characterize Informatics.

Computational Science and Informatics have now converged and have become inexorably intertwined, especially in disciplines central to CLS. There is substantial technology crossover, e.g. with matrix methods being applied to data mining and search algorithms drawing on numerical techniques. The CS & I pillar will leverage and foster these internal synergies (between CS and I) through research and training within the discipline, while simultaneously contributing, as a tool and enabling technology, to advances in the life sciences, engineering, chemistry, psychological and brain sciences, and increasingly to social science and humanities disciplines as well.