Nikhil Karamchandani, University of California at San Diego
Wednesday Oct 20th. 2010
Abstract: The general problem of computation over networks can be used to model many different scenarios, ranging from environmental monitoring to intrusion detection. The goal in such problems is to design efficient schemes for computing different target functions over various network topologies.
In the first part of the talk, we will model the problem as a generalization of “network coding” and attempt to characterize the maximum “rate of computation”. A cut-based upper bound is proposed and we study the tightness of this bound for different target functions and network topologies.
The second part of the talk will focus on a model more suitable for real dynamic networks. In such networks, it is infeasible to continuously adapt the operations at all nodes according to the changing network topology or demand function. Hence, we will restrict most nodes in the network to always perform the same operation (in particular, randomized linear network coding) and only some nodes will change operations depending on the the current target function/topology. We will study efficient computation schemes for different functions in this model.
Bio: Nikhil Karamchandani received the B.Tech degree in Electrical Engineering from the Indian Institute of Technology, Bombay in 2005, the M.S. degree in Electrical Engineering from the University of California at San Diego in 2007, and is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering, University of California at San Diego. His research interests are in communication theory and include network coding, information theory, and random graphs. He received the California Institute for Telecommunications and Information Technology (CalIT2) fellowship in 2005.
Host: Alex Dimakis, dimakis [at] usc.edu
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