Research
Arman Mielke works on machine learning for combinatorial optimization. His research uses graph neural networks to guide existing heuristic solvers and develops preference-based gradient estimators for training through nondifferentiable optimization procedures. The aim is to improve solution quality while retaining the feasibility guarantees and computational advantages of established heuristics.
Selected publications
Information reviewed 26 September 2026 · University profile