This report presents challenges, opportunities, and directions for computational science and engineering (CSE) research and education for the next decade. Over the past two decades the field of CSE has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers with algorithmic inventions and software systems that transcend disciplines and scales. CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society, and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution and increased attention to data-driven discovery, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. With these many current and expanding opportunities for the CSE field, there is a growing demand for CSE graduates and a need to expand CSE educational offerings. This need includes CSE programs at both the undergraduate and graduate levels, as well as continuing education and professional development programs, exploiting the synergy between computational science and data science. Yet, as institutions consider new and evolving educational programs, it is essential to consider the broader research challenges and opportunities that provide the context for CSE education and workforce development.


  1. computational science and engineering
  2. education
  3. high-performance computing
  4. large data analytics
  5. predictive science

MSC codes

  1. 97-02
  2. 0A-72
  3. 01-08
  4. 68U20
  5. 68W99
  6. 97A99
  7. 65Y99
  8. 65Y05
  9. 68N99
  10. 62-07

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Information & Authors


Published In

cover image SIAM Review
SIAM Review
Pages: 707 - 754
ISSN (online): 1095-7200


Submitted: 3 October 2016
Accepted: 28 November 2017
Published online: 8 August 2018


  1. computational science and engineering
  2. education
  3. high-performance computing
  4. large data analytics
  5. predictive science

MSC codes

  1. 97-02
  2. 0A-72
  3. 01-08
  4. 68U20
  5. 68W99
  6. 97A99
  7. 65Y99
  8. 65Y05
  9. 68N99
  10. 62-07



Funding Information

National University of Singapore (NUS) https://doi.org/10.13039/501100001352
U.S. Department of Energy (DOE) https://doi.org/10.13039/100000015 : DE-AC02-06CH11357

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