Matthias Mnich

Matthias Mnich is a Professor and Head of the Institute for Algorithms and Complexity at Hamburg University of Technology (TUHH), within the School of Electrical Engineering, Computer Science and Mathematics. He also serves as Deputy Dean International, reflecting his leadership in academic administration and international collaboration. He is a principal investigator at the Helmholtz Graduate School for the Structure of Matter, further emphasizing his interdisciplinary impact.

His research lies at the intersection of theoretical computer science and practical algorithm design, focusing on parameterized algorithms, approximation algorithms, combinatorial optimization, scheduling, and algorithmic game theory. His work often bridges theoretical guarantees with real-world applications in energy systems, quantum computing, and logistics.

The recent publications (2023–2025) highlight his sustained excellence in top-tier venues such as FOCS, ICALP, ESA, STACS, and journals like Mathematical Programming and ACM Transactions on Algorithms. These works explore foundational problems in vector bin packing, integer programming, graph algorithms, and kernelization, while also applying algorithmic techniques to microgrid energy optimization and quantum algorithm engineering.

He is deeply embedded in the theoretical computer science community, having served on program committees of major conferences including:

• STACS 2023
• ESA 2024
• FOCS 2023
• ICALP 2024
• IJCAI 2019–2025
• AAAI 2018
• SWAT 2018

He has successfully supervised several PhD students to completion, including Matthias Kaul, Roland Vincze, and Alexander Göke, many of whom have taken postdoctoral positions at institutions like the University of Bonn and University of Augsburg. His current research projects include PATTERN (2025–2031), Hamburg Quantum Computing (2024–2029), and Kernelization for Big Data, indicating long-term funding and strategic research directions. He leads the Institute for Algorithms and Complexity (E-11), fostering a research environment focused on high-impact algorithmic research.