Prof. Dr. Nathan Sudermann-Merx

Profil

  • 2021-ongoing: Professor at Cooperative State University Mannheim
  • 2017-2021: Decision Scientist (Machine Learning and Optimization) at BASF in Ludwigshafen
  • 2016-2017: Operations Research Analyst at EnBW in Karlsruhe
  • 2016: Operations Research (PhD, with distinction) at Karlsruhe Institute of Technology (KIT)
  • 2013: Mathematics (Master's degree, with distinction) at Karlsruhe Institute of Technology (KIT)
  • 2011: Industrial Engineering (Bachelor's degree) at Karlsruhe Institute of Technology (KIT)

Visit my website

  • Mathematics for Computer Scientists
  • Mathematical Optimization

  • Machine Learning and Mixed-Integer Optimization
  • Large-Scale Mixed-Integer Optimization Problems and Decomposition Methods
  • Generalized Nash Equilibrium Problems

Books

Publications in Peer-Reviewed Journals

  • A. Thebelt, C.Tsay, R. M. Lee, N. Sudermann-Merx, D. Walz, B. Shafei, R. Misener, Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces, NeurIPS, 2022.
  • A. Thebelt, C. Tsay, R. M. Lee, N. Sudermann-Merx, D.Walz, T. Tranter, R. Misener, Multi-objective constrained optimization for energy applications via tree ensembles, Applied Energy, accepted, 2022.
  • A. Thebelt, J. Kronqvist, M. Mistry, R. M. Lee, N. Sudermann-Merx, R. Misener, ENTMOOT: A Framework for Optimization over Ensemble Tree Models, Computers and Chemical Engineering, accepted, 2021.
  • N. Sudermann-Merx, S. Rebennack, C. Timpe, Crossing Minimal Edge-Constrained Layout Planning, Production and Operations Management, accepted, 2021.
  • N. Sudermann-Merx, S. Rebennack, Leveraged Least Trimmed Absolute Deviations, OR Spectrum, accepted, 2021.
  • S. Sagratella, M.S. Schmidt, N. Sudermann-Merx, The noncooperative fixed charge transportation problem, European Journal of Operational Research, 284(1), 373-382, 2020.
  • C. Neumann, O. Stein, N. Sudermann-Merx, Granularity in nonlinear mixed-integer optimization, Journal of Optimization Theory and Applications, 184(2), 433–465, 2020.
  • C. Neumann, O. Stein, N. Sudermann-Merx, A feasible rounding approach for mixed-integer optimization problems, Computational Optimization and Application, 72(2), 309–337, 2019.
  • O. Stein, N. Sudermann-Merx, The noncooperative transportation problem and linear generalized Nash games, European Journal of Operational Research, 266(2), 543-553, 2018.
  • A. Dreves, N. Sudermann-Merx, Solving linear generalized Nash equilibrium problems numerically, Optimization Methods and Software, 31(5), 1036-1063, 2016.
  • O. Stein, N. Sudermann-Merx, The cone condition and nonsmoothness in linear generalized Nash games, Journal of Optimization Theory and Applications, 170(2), 687-709, 2016.
  • O. Stein, N. Sudermann-Merx, On smoothness properties of optimal value functions at the boundary of their domain under complete convexity, Mathematical Methods of Operations Research, 79(3), 327-352, 2014.

Publications in Conference Proceedings

  • A. Thebelt, J. Kronqvist, R.M. Lee, N. Sudermann-Merx, R. Misener, Global Optimization with Ensemble Machine Learning Models, Computer Aided Chemical Engineering 48, 1981-1986, 2020.

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