Abstract: Three-dimensional ultrasound is a powerful imaging technique, but it requires thousands of sensors and complex hardware. The discovery of compressive sensing has shown that the signal structure can be exploited to reduce the burden posed by traditional sensing requirements. In this talk, we will give an overview of how compressive sensing can be used... Read more →

Abstract: Reinforcement Learning (RL) has emerged as a promising paradigm for addressing sequential optimization problems when the dynamics of the underlying systems are unknown. The primary objective in RL is to learn a policy that maximizes expected future rewards, or value functions. This is typically achieved through learning the optimal value functions or, alternatively, the... Read more →