Within the wide field of sparse approximation, convolutional sparse coding (CSC) has gained increasing attention in recent years by assuming a global structured convolutional dictionary. While several works have been devoted to the practical aspects of this model, a systematic theoretical understanding of CSC seems to have been left aside.

This talk will provide a high-level overview of the various technologies that enable autonomous vehicles. We will first review the technological history of self-driving cars before condensing the common themes into a technology stack that is required to achieve fully autonomous driving. The talk will conclude with some contemporary examples.

(NOTE: SPACE IS LIMITED)

Computing the singular values and singular vectors of a large matrix is a basic task in high dimensional data analysis with many applications in computer science and statistics. In practice, however, data is often perturbed by noise. A natural question is the following. How much does a small perturbation to the matrix change the singular values and vectors? Classical (deterministic) theorems, such as those by Davis-Kahan, Wedin, and Weyl, give tight estimates for the worst-case scenario. In this talk, I will consider the case when the perturbation is random.

 This is not your usual Abstract since the intent of this presentation is to give a selected overview of my research at USF and explore areas of potential joint work during my sabbatical at UCSD and possible future collaborations. There are two thematic areas of my research: Wireless Body Area Networks and Optimization of 5G Networks.

Some of the topics that I will cover are: