Dominik Michael Krupke
PostDoc Researcher/Teacher/Consultant at TU Braunschweig, Algorithms Division.
Theoretical Mind, Practical Solutions: Mastering NPHard Optimization Problems.
I am interested in most aspects of algorithms and data structures, but my expertise lies in optimizing difficult, i.e., NPhard, combinatorial problems with various techniques. My dissertation showcases large parts of my toolkit, including Mixed Integer Programming (Gurobi and CPLEX), Constraint Programming (CPSAT of Google’s ortools), SATsolvers, Dynamic Programming, Graph Algorithms, Approximation Algorithms, Reinforcement Learning, MetaHeuristics, etc. There is a large overlap with the fields of Operations Research and Mathematical Optimization, but I prefer the term Algorithm Engineering as I am really focused on the algorithms and have primarily been educated by theoretical computer scientists. Thus, I am not just interested in just applying and combining these tools but also to understand how and why they work so well (or don’t). Currently, I am especially fascinated by CPSAT because it smartly combines a lot of techniques, some of which seem unwise on first sight but work surprisingly well.
My preferred programming languages are Python and C++ (if Python is too slow or if I want to play with low level stuff).
Outside the university, I like to test and expand my physical limits in weight lifting (currently at nearly 2.5x own body weight in DL) and Krav Maga. My preferred means of transport have two wheels (cyclocross bicycle and motorcycles).
Technical Skills
 Combinatorial Optimization: Proficient in solving NPhard problems to optimality using techniques like Mixed Integer Programming, Constraint Programming, custom branchandbound algorithms, SATsolvers, and Second Order Cone Programming.
 Approximation & Metaheuristics: Skilled in finding nearoptimal solutions via approximation algorithms, metaheuristics, and LNSvariants.
 Algorithmic Foundations: Strong background in theoretical computer science, with a comprehensive understanding of algorithmic concepts and their practical applications, such as complexity, approximation, and graph theory.
 Programming & Performance: Adept in writing maintainable Python and C++ code for complex algorithms, with expertise in performance tuning and modularization. Check out my repositories for examples.
 Data Analysis & Visualization: Capable of managing, evaluating, and qualitychecking complex data, as well as visualizing data sets for insights and decisionmaking. Refer to my dissertation for exemplary empirical evaluations.
 Machine Learning: Familiar with machine learning techniques and have applied them successfully in research projects. Eager to explore their potential to augment classical algorithms, although this is neither a primary focus nor an area of expertise.
Research Skills
 TheoryPractice Bridge: Skillful in bridging the gap between theoretical computer science and practical implementation, highlighted by interdisciplinary collaborations and consultancy.
 Interdisciplinary Collaboration: Extensive involvement in projects across diverse fields such as robotics, bioinformatics, automotive, and satellite management.
 Creativity & Curiosity: Demonstrated curiosity and creativity in learning and combining new techniques, as evidenced by the diverse techniques used in my dissertation and the variety of projects undertaken.
Soft Skills
 Teaching & Presentation: Proficient in teaching and presenting complex topics, as proven by the positive evaluation of my lecture on algorithm engineering and the popularity of my online material.
 Project Management: Experienced in managing multiple projects and student teams concurrently, as evidenced by the number of successfully completed projects.
Selected Publications

 Robust disease module mining via enumeration of diverse prizecollecting Steiner treesBioinformatics, Jan 2022
 NearOptimal Coverage Path Planning with Turn CostsIn 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX) , Jan 2024