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Dolores Romero Morales

From Wikipedia, the free encyclopedia

María Dolores Romero Morales (born 5th August 1971)[1] is a Spanish operations researcher and professor of operations research at the Copenhagen Business School. Topics in her research include supply chain management, revenue management, and data mining.

Education and career

Romero studied mathematics at the University of Seville, earning bachelor's and master's degrees there in 1994. She completed a Ph.D. in operations research at Erasmus University Rotterdam in 2000.[2] Her dissertation, Optimization Problems in Supply Chain Management, was jointly promoted by Jo van Nunen and H. Edwin Romeijn.[3]

She was an assistant professor of business administration at the University of Cádiz from 1995 to 1996, and at the University of Seville in 1998. After completing her doctorate, she became an assistant professor in operations research in the department of quantitative economics at Maastricht University in 2000. She moved to the University of Oxford in 2003 as a lecturer and fellow of St Cross College, Oxford; at Oxford, she was promoted to reader in 2006 and professor in 2012. She moved to the Copenhagen Business School in 2014.[2]

Contributions

Romero is the editor-in-chief of TOP, a Spanish journal in operations research, for 2020 to 2022.[4] She is also the coordinator of the Network of European Data Scientists (NeEDS), an EU project founded in 2019 that aims to apply big data analysis to coronavirus-related predictions.[5]

References

  1. ^ Birth year from VIAF authority control record, accessed 2020-09-18
  2. ^ a b Curriculum vitae (PDF), 15 September 2020, retrieved 2020-09-18
  3. ^ Dolores Romero Morales at the Mathematics Genealogy Project
  4. ^ Nottrott, Lili (7 January 2020), "Dolores Romero Morales appointed Editor-in-Chief of TOP, the Operations Research Journal of the Spanish Society of Statistics and Operations Research", Network of European Data Scientists
  5. ^ Christensen, Kasper (14 May 2020), "CBS-related EU-project is building a 'predictor' to foresee health-related effects of Covid-19", CBS Wire, Copenhagen Business School

External links

This page was last edited on 5 October 2023, at 17:53
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