Welcome!
This website gives a brief overview on my research, (third-party funded) projects, and professional background with further references. For current teaching, please visit the website of my working group. If you like to contact me, feel free to do so via [firstname].[lastname]@uni-siegen.de.
Academic Background & Appointments
As of September 2025, I am an interim professor for Network and Data Science Management at the University of Siegen, Germany. With a neatless transition, I led the chair of Management Science at the University of Siegen before and since April 2024.
Since 2020, I am also a research fellow at the
University of Bonn, Germany,
where I have been affiliated with the High Performance Computing and Analytics Lab until my engagement in Siegen.
Moreover, I am elected to the steering committee of the transdisciplinary
research area Mathematics, Modelling and Simulation of Complex Systems at the University of Bonn.
Prior to these appointments, I have been an interim full professor for Discrete Optimization
as well as a postdoc at the Department of Mathematics & Computer Science at the University of Cologne.
In 2015, I received a PhD (Dr. rer. nat.) from the Department, working
with Michael Jünger. My diploma is in Computer Science, received from the Technical University of Dortmund, Germany, in 2008.
PhD Students
Besides my small team in Siegen, and thanks to my granted DFG project, Chris Cohadari is my PhD student at the University of Bonn since 2025.
Research
I am working in mathematical programming, operations research and business analytics, on the edges between and with a variety of applications in quantitative economics, computer science, and mathematics. When asked to cut down the ultimate goal of my research into a short sentence, then it is to push frontiers in the solution of practically relevant optimization problems. This means in particular to increase the size or share of problem instances that can be solved routinely, by means of better algorithms, models, and data-driven analyses.
To achieve this goal, I frequently develop and combine methods from
Discrete and Continuous Optimization (especially Linear, (Convex) Quadratic, and Semidefinite Programming),
Network Analysis and Data Science, as well as Algorithm Engineering.
The applications are interdisciplinary respectively come from various disciplines, for instance from logistics and transportation, production and manufacturing, health-care, and the automotive industry. On a meta-level, I gained a particular expertise in ordering, assignment, scheduling and binary quadratic problems which are ubiquitous.
Please find below some example projects and publications.
Selected Lines of Research
This a selection from my major research directions and projects with recent developments and publications.
1. Sophisticated Models and Algorithms for Applications
This line of research focusses on the solution of various combinatorial optimization problems that arise as economic and interdisciplinary applications. Specifically, it is my passion to design and improve mathematical models with emphasis on their better practical solution, and to develop sophisticated solution techniques that go far beyond “plugging a model into a solver”. Methods of mine have been applied in business practice, e.g. by Siemens for manufacturing projects. Currently, I run projects on further industrial as well as health-care applications. The most recent publications deal with:
- Elective surgery respectively operating room scheduling in health-care operations management, a paper presented at ISCO 2026.
- The line-based Dial-a-Ride problem in public transportation, this is joint work with Antonio Lauerbach, Kendra Reiter, and Marie Schmidt, and a preprint is available on arXiv.
- For the Quadratic Linear Ordering Problem, I showed which of the well-known triangle inequalities are actually facet-defining and how these can be distinguished from the others, the article appeared in Optimization Letters.
- The Target Visitation Problem which models route planning applications with additional preferences between the locations to visit, beyond the classical travel distance or cost. The paper has been presented at Symposium on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS) 2025.
- Asymmetric Betweenness Problems with applications in facility layout and relations to the more general Quadratic Linear Ordering Problem.
2. Quadratic Unconstrained Binary Optimization (QUBO) and the Maximum Cut Problem (MaxCut)
I work on exact methods for these two strongly related problems from various perspectives. Thereby, an already tremendous and steadily increasing amount of applications from various disciplines is formulated and solved as a QUBO.
- Integration and common enhancement of solution techniques from Polyhedral Combinatorics, Integer Linear Programming, and Semidefinite Programming in a collaboration with Angelika Wiegele. A corresponding DFG project has been granted and started in 2025.
- For the QUBO perspective, there are two recent preprints: One is on enabling column generation approaches for linearized QUBOs. And the other one is on integrating convex curvature information into linear relaxations for QUBO.
- For MaxCut, there are recent works on “Spanning-Tree Integrality”, i.e., on Mixed-Integer Linear and Semidefinite Programming formulations that require only the edge variables corresponding to a spanning tree to be integral.
- Techniques for the separation of facet-defining odd-cycle inequalities, giving a leap to exact solvers: One and Two
- Exact solvers for sparse instances based on Integer Programming, like McSparse
- The Spin Glass Server
- A new instance library with meta data information, see a prototype here
3. General Methods for Constrained Binary Quadratic Optimization
These works are generally applicable to a wide range of applications, e.g.\ involving assignment or matching constraints.
- I develop the theory and work on the computational effectiveness of the Inductive Linearization Technique for binary programs with linear constraints and a quadratic objective function, quadratic constraints, or both. Effectively, it generates a (often small) subset of the constraints defining a first-level RLT of the original program which - by construction - suffice to linearize it without any further effort. By the way, besides that the Inductive (respectively Compact) Linearization Technique is used in industry, according to e.g. Springer, the according papers are cited much more often by serious publications than reflected by Google Scholar (for reasons I do not know). Compare in particular this one (citations are also listed explicitly when logged in).
- In 2023/2024, I worked out an inexact simplex algorithm that performs quadratic optimization over the vertices of (0-1) polyhedra.
Publications
- A full publication list (i.e., with theses, recent articles to appear, as well as preprints) is here.
- A list of peer-reviewed publications (and selected preprints) is available via ORCID.
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Yes, I do also have a Google Scholar profile.
- A disclaimer first: For several papers of mine, Google Scholar provably misses a substantial number of citations from serious referencing publications since years (!) (for reasons I do not know and cannot change, annoyingly), with an according impact on measures. This applies e.g. for the papers on the Inductive (respectively Compact) Linearization Technique, in particular (but not only) this one is cited much more often according to Springer, with an explicit list after login.
- Most of the time, the profile is accessible directly via Google Scholar. Moreover, if you have been provided with a copy of my CV, you will find a direct link also there. In any other case, feel free to contact me.
- Finally, you might also take a look at DBLP, however, as you see some of the rather math-oriented journal publications are either not listed there or only listed with a quite large delay.