Large Monitoring Systems: Data Analysis, Design and Deployment

Seminar
Monday, April 06, 2009
11:30am
ENS 637

AbstractThe emergence of pervasive sensing, high bandwidth communications and inexpensive data storage and computation systems makes it possible to drastically change how we design, monitor and regulate very large-scale physical and human networks. Performance gains in the way we operate these networks translate into large savings. There are many critical challenges to create functional monitoring systems, such as data reliability, computational efficiency and proper system design, including choices of sensors, communication protocols, and analysis approaches.
In this talk I present a framework to design a monitoring system, deploy it, maintain it and process the incoming heterogeneous sources of information, resulting in new applications. The framework is being applied to urban traffic monitoring, road infrastructure sensing and patient safety. We have developed various state of the art statistical inference algorithms, computed performance guarantees and studied some of the fundamental limits of the proposed ideas. I illustrate the methodology using experimental deployments we have built and that are currently in use.

BiographyRam Rajagopal is currently a Ph.D. candidate in EECS, advised by Prof. Pravin Varaiya, and M.A. candidate in Statistics at the University of California, Berkeley. He holds a M.S. in ECE from the University of Texas, Austin and a B.S. in EE from the Federal University of Rio de Janeiro. Ram was a DSP research engineer at National Instruments, where he created industrial machine vision, controls and embedded systems products, and a visiting researcher at IBM Research, where he worked on analytics for early warning systems. He is currently interested in experimental and theoretical research in signal processing, statistics and control with a focus in systems for monitoring and controlling complex networks, in particular, to improve transportation and infrastructure networks, and increase healthcare safety. His work has received various awards, and led to several publications, more than 40 patents, 10 commercial products and 3 startup companies.

Speaker

Ph.D Candidate
UC Berkeley

BiographyRam Rajagopal is currently a Ph.D. candidate in EECS, advised by Prof. Pravin Varaiya, and M.A. candidate in Statistics at the University of California, Berkeley. He holds a M.S. in ECE from the University of Texas, Austin and a B.S. in EE from the Federal University of Rio de Janeiro. Ram was a DSP research engineer at National Instruments, where he created industrial machine vision, controls and embedded systems products, and a visiting researcher at IBM Research, where he worked on analytics for early warning systems. He is currently interested in experimental and theoretical research in signal processing, statistics and control with a focus in systems for monitoring and controlling complex networks, in particular, to improve transportation and infrastructure networks, and increase healthcare safety. His work has received various awards, and led to several publications, more than 40 patents, 10 commercial products and 3 startup companies.