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: Expert in solving NPhard problems using advanced techniques such as MIP, CP, SAT, BnB, and SOCP.
 Approximation & Metaheuristics: Demonstrated skill in achieving nearoptimal solutions via approximation algorithms, metaheuristics, and LNSvariants.
 Algorithmic Foundations: Solid grounding in theoretical computer science and algorithmic concepts.
 Programming & Performance: Proficient in Python and C++, with a focus on performance optimization and modular code architecture.
 Data Analysis & Visualization: Skilled in data management, evaluation, and visualization techniques.
 Machine Learning: Basic familiarity with ML techniques; experience in applying them in select research projects.
Research Skills
 TheoryPractice Bridge: Specialized in connecting theoretical insights with practical applications.
 Interdisciplinary Collaboration: Proven track record in robotics, bioinformatics, automotive industry, and satellite management.
 Creativity & Curiosity: Highly creative and curious about exploring and integrating new techniques.
Soft Skills
 Teaching & Presentation: Capable of teaching and presenting intricate topics in an understandable manner.
 Project Management: Experienced in managing diverse projects and student teams.
 Public Engagement: CoOrganizer and technical lead of the Geometric Optimization Challenges at SoCG for multiple years, showcasing a commitment to community outreach.
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