Deep neural networks are widely used for nonlinear function approximation with applications spanning from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties. In this talk, Changliu Liu,
Former CARS Fellow, assistant professor at CMU will present methods that have emerged recently for soundly verifying such properties.