Dominik Michael Krupke

PostDoc Researcher/Teacher/Consultant at TU Braunschweig, Algorithms Division.

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Theoretical Mind, Practical Solutions: Mastering NP-Hard Optimization Problems.

I am interested in most aspects of algorithms and data structures, but my expertise lies in optimizing difficult, i.e., NP-hard, combinatorial problems with various techniques. My dissertation showcases large parts of my toolkit, including Mixed Integer Programming (Gurobi and CPLEX), Constraint Programming (CP-SAT of Google’s ortools), SAT-solvers, Dynamic Programming, Graph Algorithms, Approximation Algorithms, Reinforcement Learning, Meta-Heuristics, 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 CP-SAT 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 NP-hard problems using advanced techniques such as MIP, CP, SAT, BnB, and SOCP.
  • Approximation & Meta-heuristics: Demonstrated skill in achieving near-optimal solutions via approximation algorithms, meta-heuristics, and LNS-variants.
  • 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

  • Theory-Practice 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: Co-Organizer and technical lead of the Geometric Optimization Challenges at SoCG for multiple years, showcasing a commitment to community outreach.

Selected Publications

  1. msc.png
    Minimum Scan Cover with Angular Transition Costs
    Sándor P. Fekete ,  Linda Kleist ,  and  Dominik Krupke
    SIAM J. Discret. Math., 2021
  2. robust_preview.png
    Robust disease module mining via enumeration of diverse prize-collecting Steiner trees
    Judith Bernett ,  Dominik Krupke ,  Sepideh Sadegh ,  Jan Baumbach ,  Sándor P. Fekete ,  Tim Kacprowski ,  Markus List ,  and  David B. Blumenthal
    Bioinformatics, Jan 2022
  3. pcpp_example.png
    Near-Optimal Coverage Path Planning with Turn Costs
    Dominik Krupke
    In 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX) , Jan 2024