معرفی
Miguel F. Anjos is a Professor in the School of Mathematics at the University of Edinburgh, where he serves as Deputy Head of School and Head of the research theme Data and Decisions. He also holds an Associate Professor position at Polytechnique Montréal in the Department of Mathematics and Industrial Engineering. Additionally, he chairs the Mathematical Optimization Society (2023-2025) and serves as Vice-President (International Activities) of INFORMS.
His research focuses on mathematical optimization to solve large-scale discrete nonlinear optimization problems in engineering applications, particularly in conic optimization for facility layout problems and optimal power flows in electricity grids. His work addresses critical challenges in smart grid development, including efficient network utilization, renewable energy integration, energy storage, and customer participation in grid operations. He has extensive industry experience working with National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, and EDF.
His publications reveal a strong focus on optimization methods applied to energy systems, with recent work spanning bilevel optimization, robust optimization with uncertainty quantification, electric vehicle infrastructure planning, and carbon credit markets. His research consistently bridges theoretical optimization with practical engineering applications.
- Canada Research Chair in Nonlinear Discrete Optimization in Engineering (2011-2016)
- NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair in Smart Grid Optimization (2016-2019)
- INRIA International Chair for Optimizing Smart Grids (2016-2021)
- Winner of MOPTA 2020 and 2024 competitions
- Queen Elizabeth II Diamond Jubilee Medal (2012)
- Fellow of the Humboldt Foundation, EUROPT, and Canadian Academy of Engineering
Professor Anjos has supervised numerous PhD students and postdoctoral fellows, with graduates securing positions at academic institutions including University of Tennessee, Western Ontario University, and industry roles at Amazon, CAISO, and major consultancies. He maintains three benchmark datasets (QAPLIB, FLPLIB, and the Jones Benchmark) as a service to the optimization community.
