Papers
Research from TU Delft: decision-focused learning and graph neural networks.
Learning Feasible Decisions Directly from Features for Combinatorial Optimization
Cristian Turcan (first author)
TU Delft Honours Programme · under review at BNAIC 2026
Proposes LR-STE, a solver-free predict-then-optimize method that projects a
neural predictor's output onto the problem's linear relaxation and applies a feasibility-correction
layer, trained end-to-end with a straight-through estimator to minimise decision regret rather than
prediction error. Benchmarked on 0/1 Knapsack and the Travelling Salesperson Problem against SPO+,
two-stage predict-then-optimize, and projection-free (STE) baselines.
Heterophilic Methods on Multi-Label Graphs
Cristian Turcan (sole author)
TU Delft Bachelor Research Project (CSE3000) · 2026
Benchmarked 8 models: MLP and DeepWalk baselines plus 6 heterophily GNNs (H2GCN, GPR-GNN, LINKX,
FAGCN, ACM-GCN, Ordered GNN), across 5 real and 2 synthetic multi-label graphs in PyTorch Geometric.
Controlled homophily and label-cardinality sweeps showed that node-classification performance tracks
graph homophily more than model sophistication. Built gnnbench, a config-driven pipeline with
a synthetic multi-label graph generator, hardened with split-leakage tests and parallelised as SLURM
array jobs on TU Delft's DelftBlue cluster.