Marc Huber

Marc Huber is a PreDoc Researcher (academic rank: Researcher) at the Institute of Logic and Computation within the Faculty of Informatics at TU Wien. Currently on leave, he conducts research at the intersection of combinatorial optimization and machine learning, focusing on algorithmic innovations for complex scheduling and sequence problems.

His core research interests include:

• Combinatorial Optimization
• Machine Learning for Optimization
• Beam Search and Metaheuristics
• Staff and Flow Shop Scheduling
• Sequence Problems (Longest/Shortest Common Subsequence)
• Algorithm-ML Integration

Huber's publication trajectory (2021-2024) reveals a consistent methodological evolution: progressively integrating reinforcement learning and value function approximation into beam search frameworks. This approach has yielded significant advances in staff rostering, multi-agent path finding, and sequence alignment, demonstrating how learned heuristics outperform traditional optimization techniques in solution quality and computational efficiency.

Scientific Awards: No awards, fellowships, or medals were documented in available sources.

As a thesis supervisor, Huber has guided four master's students at TU Wien through research on graph neural networks for clique problems, reinforcement learning for flow shop scheduling, e-mobility fleet scheduling, and staff resortering algorithms. His advising emphasizes practical implementation of learning-based optimization techniques.

He operates within the Algorithms and Complexity research group (E192-01), collaborating closely with Prof. Raidl on algorithm engineering projects funded through TU Wien's research infrastructure. Current work focuses on extending learning beam search to multi-objective combinatorial problems.