  Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has
enabled rapid development of sophisticated and autonomous systems. However, the
inherent complexity to rigorously assure the safe operation of such systems
hinders their real-world adoption in safety-critical domains such as aerospace
and medical devices. Hence, there is a surge in interest to explore the use of
advanced mathematical techniques such as formal methods to address this
challenge. In fact, the initial results of such efforts are promising. Along
these lines, we propose the use of quantifier elimination (QE) - a formal
method technique, as a complimentary technique to the state-of-the-art static
analysis and verification procedures. Using an airborne collision avoidance DNN
as a case example, we illustrate the use of QE to formulate the precise range
forward propagation through a network as well as analyze its robustness. We
discuss the initial results of this ongoing work and explore the future
possibilities of extending this approach and/or integrating it with other
approaches to perform advanced safety assurance of DNNs.
