Alexander Eckl
PhD Student
Education:
- since 05/2018: PhD candidate, Research Training Group "Advanced Optimization in a Networked Economy”, TUM
- 04/2015 - 03/2018: Master of Science, Mathematics in Operations Research, TUM
- 08/2015 - 01/2016: Erasmus: KTH Royal Institute of Technology, Stockholm, Sweden
- 10/2011 - 03/2015: Bachelor of Science, Mathematics, TUM
Research Interests:
My interests lie in discrete and combinatorial optimization, particularly optimization on graphs, network flows, algorithmic optimization and approximation. I am also researching uncertainty in the form of robust optimization.
Publications:
Susanne Albers and Alexander Eckl (2021) Scheduling with Testing on Multiple Identical Parallel Machines. In: Lubiw A., Salavatipour M. (eds) Algorithms and Data Structures. WADS 2021. Lecture Notes in Computer Science, vol 12808. Springer, Cham. https://doi.org/10.1007/978-3-030-83508-8_3
Susanne Albers and Alexander Eckl (2021) Explorable Uncertainty in Scheduling with Non-uniform Testing Times. In: Kaklamanis C., Levin A. (eds) Approximation and Online Algorithms. WAOA 2020. Lecture Notes in Computer Science, vol 12806. Springer, Cham. https://doi.org/10.1007/978-3-030-80879-2_9
Alexander Eckl, Anja Kirschbaum, Marilena Leichter, Kevin Schewior. A stronger impossibility for fully online matching. Operations Research Letters, Volume 49, Issue 5, Pages 802-808, 2021. https://doi.org/10.1016/j.orl.2021.08.012
Master's Thesis: Variations of the robust network flow problem
Bachelor's Thesis: Affine-scaling-methods for data classification with support vector machines