Events

PhD Defense: "Scalable Approaches to Communication and Inference: Minimalistic strategies for measurement and coordination"

Dinesh Ramasamy

August 27th (Wednesday), 10:00am
Harold Frank Hall (HFH), Room 4164


Emerging engineered systems dwarf their predecessors in scale. As a result, minimalistic design approaches that extract the essential features of the problem at hand have become compelling. Adopting such designs from the very outset enables us to decrease the problem scale to manageable levels. In this talk, we consider two examples which illustrate the benefits of this approach.

We first consider the problem of estimating continuous valued parameters from a few random projections of a high dimensional signal (compressive measurements). A direct application of standard compressed sensing based on discretization suffers from performance loss due to basis mismatch. We show that this is not an inherent limitation of compressive measurements. To this end, we consider lower bounds on estimation error variance, the Cramer Rao Bound (CRB) and the Ziv-Zakai Bound (ZZB) and show that random projections preserve these bounds up to an SNR penalty equal to the dimensionality reduction factor. We show how the convergence of the ZZB to the CRB can be used to tightly predict the number of compressive measurements needed to avoid gross estimation errors. We illustrate these ideas using the example of channel estimation for large 60GHz arrays.

The second problem we consider is that of identifying a user’s interests from just his/her tweet times. By using the known timing of “events” associated with a topic (such as the times when a baseball team plays its games), we are able to identify users interested in this topic (the baseball team). We also show how the timing of these events can themselves be inferred from aggregate Twitter feeds obtained by querying Twitter with a few keywords related to the topic.

About Dinesh Ramasamy:

Dinesh Ramasamy received his Bachelor’s degree in Electrical Engineering from the Indian Institute of Technology, Madras, in 2009 and his M.S. in Electrical and Computer Engineering from the University of California Santa Barbara in 2011. He is currently a Ph.D. candidate in the same department at UCSB. He interned with the modem team at Qualcomm, Santa Clara in 2011. His research interests include wireless communication and inference for social networks.

Hosted by: Professor Upamanyu Madhow