Personal information
Biography
Mauricio G.C. Resende completed his high school degree at Escola Americana do Rio de Janeiro (American School of Rio de Janeiro) in 1973. As an undergraduate in college, he earned an electrical engineering degree (systems engineering option) from the Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) in 1978. From September 1978 to August 1979, he was a master of science student in operations research at the Georgia Institute of Technology in Atlanta, GA, U.S., where he graduated in August 1979. He worked at the Methods and Models group (Acessoria de Métodos e Modelos) of Furnas Centrais Elétricas (a large power company in Rio de Janeiro) from 1979 to 1982. From 1982 to 1987, he completed a Ph.D. in operations research at the University of California at Berkeley. His minors at Berkeley were computer science and information systems. His PhD thesis introduced Starvation Avoidance, a closed-loop job release mechanism for semiconductor fabrication where the main source of randomness is due to machine failure and repair. From 1988 to 2014 he worked as a research scientist at AT&T Bell Labs and AT&T Labs Research, respectively, in the Mathematical Foundations of Computing and the Algorithms and Optimization Research departments. In 2014 he joined Amazon as a research scientist where he remained until 2022. He is also Affiliate Professor of Industrial and Systems Engineering at the University of Washington in Seattle since 2016. In 2016 he was elected Fellow of INFORMS (Institute for Operations Research and the Management Sciences). Since 2023 he is an independent researcher. He did pioneering work in the field of metaheuristics, having proposed with T.A. Feo the metaheuristic GRASP in 1989. Also in 1989, he published a paper (with I. Adler, G. Veiga, and N. Karmarkar) showing for the first time that interior point methods for linear programming could outperform the simplex method. Since the late 1990s, he has worked (mainly with J.F. Gonçalves) on the development of the metaheuristic Biased Random-Key Genetic Algorithms (BRKGA). He currently works in the field of heuristic methods for combinatorial optimization applied to problems in telecommunications and transportation logistics. He has authored or coauthored over 195 papers in journals or as book chapters and has edited six books, including Handbook of Heuristics" (Springer, 2018), "Handbook of Applied Optimization" (Oxford, 2002) and "Handbook of Optimization in Telecommunications" (Springer, 2006). He coauthored the book "Optimization by GRASP - Greedy Randomized Adaptive Search Procedures" (Springer, 2016). He holds 15 patents in the U.S. He is current or past member of the editorial boards of several journals, including Networks, Discrete Optimization, J. of Global Optimization, J. of Heuristics, Computational Optimization and Applications, and J. of Combinatorial Optimization.