Mauricio Resende

Mauricio G. C. Resende (born July 27, 1955 in Maceió, Brazil) is a Brazilian-American research scientist with contributions to the field of mathematical optimization. He is best known for the development of the metaheuristics GRASP (greedy randomized adaptive search procedures),[1] and BRKGA (biased random-key genetic algorithms)[2] as well as the first successful implementation of Karmarkar’s interior point algorithm.[3]

He published over 180 peer-reviewed papers, the book Optimization by GRASP[4] and co-edited five books, including the Handbook of Applied Optimization,[5] the Handbook of Optimization in Telecommunications,[6] the Handbook of Heuristics,[7] and the Handbook of Massive Datasets.[8] Additionally, he gave multiple plenary talks[9] in international conferences and is on the editorial boards of several scientific journals.

Education

In June 1978, Mauricio G. C. Resende graduated from PUC-Rio with an Electrical Engineering degree with concentration in Systems Engineering.[10] In August 1979, he earned a M.Sc. in operations research at the Georgia Institute of Technology. Later, in August 1987, he earned a Ph.D. in operations research in at the University of California, Berkeley.[11]

Career

Mauricio G. C. Resende is currently an INFORMS Fellow,[12] holds a permanent member position of DIMACS[13] at Rutgers University and is an affiliate professor at the University of Washington.[14] Until December 2022, he worked at Amazon.com as a Principal Research Scientist in the Mathematical Optimization and Planning group.[15] Previously, he was Lead Inventive Scientist at AT&T Bell Labs where he worked for over a quarter century.

References

  1. ^ Feo, Thomas A.; Resende, Mauricio G. C. (March 1995). "Greedy Randomized Adaptive Search Procedures". Journal of Global Optimization. 6 (2): 109–133. doi:10.1007/bf01096763. ISSN 0925-5001. S2CID 2110014.
  2. ^ Resende, Mauricio G.C.; Ribeiro, Celso C. (2010), "Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications", International Series in Operations Research & Management Science, Boston, MA: Springer US, pp. 283–319, doi:10.1007/978-1-4419-1665-5_10, ISBN 978-1-4419-1663-1, retrieved 2024-01-03
  3. ^ Adler, Ilan; Resende, Mauricio G. C.; Veiga, Geraldo; Karmarkar, Narendra (May 1989). "An implementation of Karmarkar's algorithm for linear programming". Mathematical Programming. 44 (1–3): 297–335. doi:10.1007/bf01587095. ISSN 0025-5610. S2CID 12851754.
  4. ^ Resende, Mauricio G. C.; Ribeiro, Celso C. (2016), "GRASP for continuous optimization", Optimization by GRASP, New York, NY: Springer New York, pp. 229–244, doi:10.1007/978-1-4939-6530-4_11, ISBN 978-1-4939-6528-1
  5. ^ Resende, Mauricio G. C.; Pardalos, Panos M., eds. (2006). Handbook of Optimization in Telecommunications. doi:10.1007/978-0-387-30165-5. ISBN 978-0-387-30662-9.
  6. ^ Resende, Mauricio G. C.; Pardalos, Panos M., eds. (2006). Handbook of Optimization in Telecommunications. doi:10.1007/978-0-387-30165-5. ISBN 978-0-387-30662-9.
  7. ^ Martí, Rafael; Pardalos, Panos M.; Resende, Mauricio G. C., eds. (2018). Handbook of Heuristics. Cham: Springer International Publishing. doi:10.1007/978-3-319-07124-4. ISBN 978-3-319-07123-7.
  8. ^ Abello, James; Pardalos, Panos M.; Resende, Mauricio G. C., eds. (2002). "Handbook of Massive Data Sets". Massive Computing. 4. doi:10.1007/978-1-4615-0005-6. ISBN 978-1-4613-4882-5. ISSN 1569-2698. S2CID 46033589.
  9. ^ "Talks". mauricio.resende.info. Retrieved 2024-01-07.
  10. ^ Resende, Mauricio. "Education and research interests". Retrieved 2024-01-07.
  11. ^ Pang, Eugene (2016-11-07). "IEOR Alum Mauricio G. C. Resende Chosen As INFORMS Fellow For Class Of 2016". UC Berkeley IEOR Department - Industrial Engineering & Operations Research. Retrieved 2024-01-07.
  12. ^ INFORMS. "Mauricio G. C. Resende". INFORMS. Retrieved 2024-01-04.
  13. ^ "DIMACS :: DIMACS Members". dimacs.rutgers.edu. Retrieved 2024-01-07.
  14. ^ "Adjunct, Affiliate & Emeritus Faculty". Industrial & Systems Engineering. 2015-10-16. Retrieved 2024-01-07.
  15. ^ "How Amazon's Middle Mile team helps packages make the journey to your doorstep". Amazon Science. 2021-04-22. Retrieved 2024-01-07.