Yew-Soon Ong

VaFM outperforms traditional methods by effectively integrating visual semantics into vehicle routing, addressing complex constraints that were previously overlooked.

Multi-agent RL agents can learn to collaborate *faster* by actively "perceiving" and aligning with each other's policy updates, rather than passively observing environment interactions.

Second-order federated learning can be made robust and practical: FedRCO overcomes instability issues and outperforms first-order methods in non-IID settings.

Scale-PINN slashes PINN training time from hours to minutes by borrowing a key principle from classical numerical solvers: iterative residual correction.